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November 2018
Administrator Håkan Hjalmarsson posted 15 November 2018
 
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Administrator Håkan Hjalmarsson posted 8 November 2018
 
Administrator Håkan Hjalmarsson posted 6 November 2018
 
October 2018
Administrator Håkan Hjalmarsson posted 29 October 2018
 
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January 2014
Administrator Håkan Hjalmarsson created event 28 January 2014
 
November 2013
Administrator Håkan Hjalmarsson created event 16 October 2013
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Fredag 229 november 2013 kl 10:15 - 12:00

A Minimum-Mean-Square Error Perspective on Model Selection
* Model selection as the minimization of the mean-square estimation error
* SURE - Stein's Unbiased Risk Estimate
* ML-estimation
Reading material Optional material  Lecturer Håkan Hjalmarsson¶

Administrator Håkan Hjalmarsson edited 16 October 2013

Fredag 29 november 2013 kl 10:15 - 121:00

Administrator Håkan Hjalmarsson changed the permissions 21 October 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administrator Håkan Hjalmarsson edited 21 October 2013

Selected Topics 45 (FEL3202 Extended Course (12 credits))

Administrator Håkan Hjalmarsson edited 23 October 2013

K53

Administrator Håkan Hjalmarsson edited 28 November 2013

Friday 29 NovTuesday 3 December 2013 at 103:15 - 114:00

Administrator Håkan Hjalmarsson edited 28 November 2013

K53

Administrator Håkan Hjalmarsson edited 28 November 2013

L31

 
Administrator Håkan Hjalmarsson created event 21 October 2013
Administrator Håkan Hjalmarsson edited 21 October 2013

Selected Topics 43 (FEL3202 Extended Course (12 credits))

Administrator Håkan Hjalmarsson edited 21 October 2013

FreTisdag 159 november 2013 kl 10:15 - 11:00

Subspace identificationFundamental properties ¶

Optional Reading ¶

Lecturer Cristian Rojas¶

Administrator Cristian Rojas edited 31 October 2013

Fundamental properties

Optional Reading ¶Lecture Slides
*
Lecturer Cristian Rojas

Administrator Cristian Rojas edited 3 November 2013

Fundamental properties

* Asymptotic covariance expressions (for high model orders)
* Proof of a.s. convergence results
Lecture Slides
* Special topics 3
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson cancelled the event 18 November 2013

Administrator Håkan Hjalmarsson changed the permissions 18 November 2013

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Administrator Håkan Hjalmarsson created event 12 November 2013
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Administrator Håkan Hjalmarsson changed the permissions 12 November 2013

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Administrator Håkan Hjalmarsson created event 11 October 2013
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Experiment Design and Closed-Loop Identification Bias/variance issues, experiment design, closed-loopSystem Estimation Methods III: Subspace identification Reading Material Ljung: Chapters 12, 13¶ Optional References Slides 8, ¶ Slides 9¶ 4.3, 7.3, 10.6¶

Optional Material
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson edited 11 October 2013

System Estimation Methods III: Subspace identification Reading Material Ljung: Chapters  4.3, 7.3, 10.6

Optional Material
* Slides 4
* Slides 5
* Max-Max problem
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson changed the permissions 14 October 2013

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Administrator Håkan Hjalmarsson edited 16 October 2013

Lecture 6Student presentations 1

Torsdag 14 november 2013 kl 101:15 - 12:00

System Estimation Methods III: Subspace identification Reading Material Ljung: Chapters  4.3, 7.3, 10.6¶ Optional Material
* Slides 4
* Slides 5
* Max-Max problem
Lecturer Cristian Roja
tudent Presentations
* Bayesian Inference¶


* Inconsistency of the maximum likelihood method¶



* Kernel method
s

Administrator Håkan Hjalmarsson edited 16 October 2013

Student Presentations
*  Bayesian Iinference
* Inconsistency of the maximum likelihood method



* Kernel methods

Administrator Håkan Hjalmarsson edited 16 October 2013

Student Presentations
*  Bayesian inference
* : Inconsistency of the mMaximum lLikelihood method¶
Estimation
* Kernel methods

Administrator Håkan Hjalmarsson edited 16 October 2013

Student Presentations
*   Bayesian inference
* : Inconsistency of Maximum Likelihood Estimation
* Kernel methods

Administrator Håkan Hjalmarsson edited 16 October 2013

Student Presentations
*   Bayesian inference
* : Inconsistency of Maximum Likelihood Estimation
*  Non-parametric identification:Kernel methods
*

Administrator Håkan Hjalmarsson edited 16 October 2013

Student Presentations
*   Bayesian inference
* : Inconsistency of Maximum Likelihood Estimation
*  Non-parametric identification:Kernel methods
*

Administrator Håkan Hjalmarsson edited 16 October 2013

Student Presentations
*   Bayesian inference
* : Inconsistency of Maximum Likelihood Estimation
*  Non-parametric identification:Kernel methods
Lecturer Håkan Hjalmarsson¶

Administrator Håkan Hjalmarsson edited 21 October 2013

Torsdag 14 november 2013 kl 11:1500 - 12:00

Administrator Håkan Hjalmarsson edited 23 October 2013

Q13

Administrator Cristian Rojas edited 31 October 2013

Student Presentations
*   Bayesian inference
* : Inconsistency of Maximum Likelihood Estimation
*  Non-parametric identification:Kernel methods
Lecturer Håkan Hjalmarsson

Administrator Cristian Rojas edited 31 October 2013

Student Presentations
* Bayesian inference
* Inconsistency of Maximum Likelihood Estimation
* Non-parametric identification: Kernel methods
Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson cancelled the event 12 November 2013
 
October 2013
Administrator Håkan Hjalmarsson created event 16 October 2013
Administrator Håkan Hjalmarsson edited 16 October 2013

Problem solving session: Exercise Set 2

, Problems (should have been handed in at previous lecture) FEL 3201 (8 credits) + FEL 3202(12 credits):   3E1, 3E2, C2a--d      

FEL 3202: 3T2, C2e-g.

Administrator Håkan Hjalmarsson edited 16 October 2013

Problems (should have been handed in at previous lecture) FEL 3201 (8 credits) + FEL 3202(12 credits):   3E1, 3E2, C2a--d   , 6G3   

FEL 3202: 3T2, C2e-g, 6T2

Administrator Håkan Hjalmarsson edited 16 October 2013

Problems (should have been handed in at previous lecture) FEL 3201 (8 credits) + FEL 3202(12 credits):   3E1, 3E2, C2a--d, 6G3   

FEL 3202: 3T2, C2e-g, 6T2

Lecturer Håkan Hjalmarsson¶

Administrator Håkan Hjalmarsson edited 29 October 2013

Problems (should have been handed in at previous lecture) FEL 3201 (8 credits) + FEL 3202(12 credits):   3E1, 3E2, C2a--d, 6G3   

FEL 3202: 3T2, C2e-g, 6T2

Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson changed the permissions 29 October 2013

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Administrator Håkan Hjalmarsson created event 16 October 2013

Administrator Håkan Hjalmarsson changed the permissions 21 October 2013

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Administrator Håkan Hjalmarsson edited 20 January 2014

L43

 
Administrator Håkan Hjalmarsson created event 16 October 2013
Administrator Håkan Hjalmarsson edited 16 October 2013

Problem solving session: Exercise Set 4

Problems (should have been handed in at previous lecture) FEL 3201 (8 credits) + FEL 3202(12 credits):   7E2, 7G1, 8E2¶

FEL 3202:  7E8, 8E4, 8G5¶

Administrator Håkan Hjalmarsson edited 16 October 2013

Problems (should have been handed in at previous lecture) FEL 3201 (8 credits) + FEL 3202(12 credits):   7E2, 7G1, 8E2, 9E3

FEL 3202:  7E8, 8E4, 8G5

Administrator Håkan Hjalmarsson edited 16 October 2013

Problems (should have been handed in at previous lecture) FEL 3201 (8 credits) + FEL 3202(12 credits):   7E2, 7G1, 8E2, 9E3

FEL 3202:  7E8, 8E4, 8G5

Lecturer Cristian Rojas¶

Administrator Håkan Hjalmarsson changed the permissions 21 October 2013

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Administrator Håkan Hjalmarsson edited 23 October 2013

K53

Administrator Håkan Hjalmarsson edited 28 November 2013

Friday 29 NovTuesday 3 December 2013 at 114:15 - 125:00

K53

Administrator Håkan Hjalmarsson edited 28 November 2013

L31

 
Administrator Håkan Hjalmarsson created event 16 October 2013
Administrator Håkan Hjalmarsson edited 16 October 2013

Student presentations
* Errors-in-variables
*  Structured
estimation
*
1: Low rank  
* Nuclear norm
approximation using local search
*  Structured estimation 2: Nuclear norm minimization

Administrator Håkan Hjalmarsson edited 16 October 2013

Student presentations
*
*  Structured estimation 1: Low rank approximation using local search
*  Structured estimation 2: Nuclear norm minimization
*  Errors-in-variables identification

Administrator Håkan Hjalmarsson edited 16 October 2013

Student presentations
*  Structured estimation 1: Low rank approximation using local search
*  Structured estimation 2: Nuclear norm minimization
*  Errors-in-variables identification

Administrator Håkan Hjalmarsson edited 16 October 2013

Student presentations
*  Structured estimation 1: Low rank approximation using local search
*  Structured estimation 2: Nuclear norm minimization
*  Errors-in-variables identification
Lecturer Cristian Rojas¶

Administrator Håkan Hjalmarsson changed the permissions 21 October 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administrator Håkan Hjalmarsson edited 21 October 2013

Fredag 22 november 2013 kl 11:1500 - 12:00

Administrator Håkan Hjalmarsson edited 21 October 2013

Fredag 22 november 2013 kl 11:0015 - 12:0015

 
Administrator Håkan Hjalmarsson created event 16 October 2013
Administrator Håkan Hjalmarsson edited 16 October 2013

Model Quality Evaluation
* A geometric framework
 ¶

Administrator Håkan Hjalmarsson edited 16 October 2013

Model Quality Evaluation
* A geometric framework
 Reading material
* A Geometric Approach to Variance Analysis in System Identification, H. Hjalmarsson and J. Mårtensson, IEEE Transactions on Automatic Control, 56(5), pp. 983-997, 2011.
* Variance Analysis in SISO Linear Systems, J. Mårtensson and H. Hjalmarsson, Draft.


Optional material  Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson edited 16 October 2013

Model Quality Evaluation
* Stochastic convergence concepts
* A geometric framework
Reading material
* A Geometric Approach to Variance Analysis in System Identification, H. Hjalmarsson and J. Mårtensson, IEEE Transactions on Automatic Control, 56(5), pp. 983-997, 2011.
* Variance Analysis in SISO Linear Systems, J. Mårtensson and H. Hjalmarsson, Draft.

Optional material  Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson edited 16 October 2013

Model Quality Evaluation
* Stochastic convergence concepts
* Stein's paradox
* Superefficiency
* Other bounds than the Cramér-Rao
* A geometric framework
Reading material
* Slides      8
* Slides 9
* A Geometric Approach to Variance Analysis in System Identification, H. Hjalmarsson and J. Mårtensson, IEEE Transactions on Automatic Control, 56(5), pp. 983-997, 2011.
* Variance Analysis in SISO Linear Systems, J. Mårtensson and H. Hjalmarsson, Draft.

Optional material  Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson edited 16 October 2013

Model Quality Evaluation
* Stochastic convergence concepts
* Stein's paradox
* Superefficiency
* Other bounds than the Cramér-Rao
* A geometric framework
Reading material
* Slides      8
* Slides 9
* A Geometric Approach to Variance Analysis in System Identification, H. Hjalmarsson and J. Mårtensson, IEEE Transactions on Automatic Control, 56(5), pp. 983-997, 2011.
* Variance Analysis in SISO Linear Systems, J. Mårtensson and H. Hjalmarsson, Draft.

Optional material  Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson edited 16 October 2013

Model Quality Evaluation
* Stochastic convergence concepts
* Stein's paradox
* Superefficiency
* Other bounds than the Cramér-Rao
* A geometric framework
Reading material
* Slides 8
* Slides 9
* A Geometric Approach to Variance Analysis in System Identification, H. Hjalmarsson and J. Mårtensson, IEEE Transactions on Automatic Control, 56(5), pp. 983-997, 2011.
* Variance Analysis in SISO Linear Systems, J. Mårtensson and H. Hjalmarsson, Draft.

Optional material  Lecturer Håkan HjalmarssonCristian Rojas

Administrator Håkan Hjalmarsson changed the permissions 21 October 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administrator Håkan Hjalmarsson edited 21 October 2013

Selected Topics 34 (FEL3202 Extended Course (12 credits))

Administrator Håkan Hjalmarsson edited 23 October 2013

L43

Administrator Cristian Rojas edited 31 October 2013

Model Quality Evaluation
* Stochastic convergence concepts
* Stein's paradox
* Superefficiency
* Other bounds than the Cramér-Rao
* A geometric framework
Reading material
* Slides 8
* Slides 9
* A Geometric Approach to Variance Analysis in System Identification, H. Hjalmarsson and J. Mårtensson, IEEE Transactions on Automatic Control, 56(5), pp. 983-997, 2011.
* Variance Analysis in SISO Linear Systems, J. Mårtensson and H. Hjalmarsson, Draft.
Lecture Slides
*


Optional material   Lecturer Cristian Rojas

Administrator Cristian Rojas edited 3 November 2013

Model Quality Evaluation
* Stochastic convergence concepts
* Stein's paradox
* Superefficiency
* Other bounds than the Cramér-Rao
* A geometric framework
Reading material
* A Geometric Approach to Variance Analysis in System Identification, H. Hjalmarsson and J. Mårtensson, IEEE Transactions on Automatic Control, 56(5), pp. 983-997, 2011.
Lecture Slides
*
Decay rate of the Cramér-Rao bound
* Barankin bound and SNR-threshold effect
* Superefficiency
* Nuisance parameters
Reading material Lecture Slides
* Special topics 4

Lecturer Cristian Rojas

 
Administrator Håkan Hjalmarsson created event 16 October 2013
Administrator Håkan Hjalmarsson edited 16 October 2013

FreTorsdag 154 november 2013 kl 10:15 - 11:00

Administrator Håkan Hjalmarsson edited 16 October 2013

Problems (should have been handed in at previous lecture) FEL 3201 (8 credits) + FEL 3202(12 credits):   4G4, 4E1, 4E5a, C3       

FEL 3202:  4E5b, C4

Lecturer Cristian Rojas¶

Administrator Håkan Hjalmarsson changed the permissions 21 October 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administrator Håkan Hjalmarsson edited 23 October 2013

Q13

 
Administrator Håkan Hjalmarsson created event 16 October 2013
Administrator Håkan Hjalmarsson edited 16 October 2013

Information contents in data
* Sufficient statistics
* Rao-Blackwells theorem
Reading material E.L Lehmann: Theory of Point Estimation¶

Optional material.  Lecturer Håkan Hjalmarsson¶

Administrator Håkan Hjalmarsson edited 16 October 2013

Information contents in data
* Sufficient statistics
* Rao-Blackwells theorem
* Exact and conditional ML
Reading material E.L Lehmann: Theory of Point Estimation

Optional material.
* Slides 3
 Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson changed the permissions 21 October 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administrator Håkan Hjalmarsson edited 23 October 2013

L31

Administrator Cristian Rojas edited 31 October 2013

Information contents in data
* Sufficient statistics
* Rao-Blackwells theorem
* Exact and conditional ML
Reading material E.L Lehmann: Theory of Point Estimation

Optional material
* Slides 3 (old)
 Lecturer Håkan Hjalmarsson

 
Administrator Håkan Hjalmarsson created event 15 October 2013
Administrator Håkan Hjalmarsson edited 15 October 2013

Problems (should have been handed in previous lecture) FEL3201 & FEL 3202: 2G3, 2E1, 2E2, 2E5      

FEL3202: 2E3,  2T4, 2D3, 2D4

Administrator Håkan Hjalmarsson edited 15 October 2013

Problems (should have been handed in at previous lecture) FEL3201 & FEL 3202: 2G3, 2E1, 2E2, 2E5      

FEL3202: 2E3,  2T4, 2D3, 2D4

Administrator Håkan Hjalmarsson edited 16 October 2013

Problems (should have been handed in at previous lecture) FEL3201 & FEL 3202: 2G3, 2E1, 2E2, 2E5, 3E1, 3E2, C2a--d      

FEL3202: 2E3,  2T4, 2D3, 2D4, 3T2, C2e-g.

Administrator Håkan Hjalmarsson edited 16 October 2013

Problems (should have been handed in at previous lecture) FEL3201 & FEL 3202: 2E1, 2E2, 2E5, 3E1, 3E2, C2a--d      

FEL3202: 2E3,  2T4, 2D3, 2D4, 3T2, C2e-g.

Administrator Håkan Hjalmarsson edited 16 October 2013

Problems (should have been handed in at previous lecture) FEL3201 & FEL 3202: 2E1, 2E2, 2E5

FEL3202: 2E3,  2T4, 2D3, 2D4,

Lecturer Håkan Hjalmarsson¶

Administrator Håkan Hjalmarsson changed the permissions 21 October 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administrator Håkan Hjalmarsson edited 23 October 2013

M35

 
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Administrator Håkan Hjalmarsson edited 11 October 2013

Lecture 154

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Administrator Håkan Hjalmarsson edited 16 October 2013

Model Selection and Model Validation
* Classic model order selection  criteria (AIC, BIC, MDL),
*  Hypothesis tests
Reading Material Ljung: Chapter 16¶

Optional References
* Slides 10
* Slides 11
Lecturer Cristian Rojas¶

Administrator Håkan Hjalmarsson cancelled the event 16 October 2013
Administrator Håkan Hjalmarsson cancelled the event 16 October 2013
Administrator Håkan Hjalmarsson cancelled the event 16 October 2013
Administrator Håkan Hjalmarsson cancelled the event 16 October 2013
Administrator Håkan Hjalmarsson edited 16 October 2013

Lecture 14f

MånTisdag 27 januari4 december 20143 kl 10:1501:00 - 102:00

Model Selection and Model Validation
* Classic model order selection  criteria (AIC, BIC, MDL),
*  Hypothesis tests
Reading Material Ljung: Chapter 16¶ Optional References
* Slides 10
* Slides 11
Lecturer Cristian Rojas¶

Administrator Håkan Hjalmarsson cancelled the event 16 October 2013
 
Administrator Håkan Hjalmarsson created event 15 October 2013
Administrator Håkan Hjalmarsson edited 15 October 2013

Lecture 2-ASelected Topics 1

Administrator Håkan Hjalmarsson edited 15 October 2013

Signal Estimation - Selected Topics
* Some concepts from probability theory
* Geometrical interpretation of estimation
* Properties of the Kalman filter
Reading material C.E. De Souza, M.R. Gevers and G.C. Goodwin: Riccati equations in optimal filtering of nonstabilizable systems having singular state transition matrices, IEEE Transactions on Automatic Control, 31(9), opp. 831-838¶

Optional material -¶

Lecturer Håkan Hjalmarsson¶

Administrator Håkan Hjalmarsson changed the permissions 15 October 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administrator Håkan Hjalmarsson edited 15 October 2013

Signal Estimation - Selected Topics
* Some concepts from probability theory
* Geometrical interpretation of estimation
* Properties of the Kalman filter
Reading material C.E. De Souza, M.R. Gevers and G.C. Goodwin: Riccati equations in optimal filtering of nonstabilizable systems having singular state transition matrices, IEEE Transactions on Automatic Control, 31(9), opp. 831-838

Optional material -

Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson edited 15 October 2013

Signal Estimation - Selected Topics
* Some concepts from probability theory
* Geometrical interpretation of estimation
* Properties of the Kalman filter
Reading material
*
C.E. De Souza, M.R. Gevers and G.C. Goodwin: Riccati equations in optimal filtering of nonstabilizable systems having singular state transition matrices, IEEE Transactions on Automatic Control, 31(9), pp. 831-838

* probability_theory_1.pdf
Optional material -

Lecturer Håkan Hjalmarsson

Administrator Håkan Hjalmarsson edited 16 October 2013

Selected Topics 1 (FEL3202 Extended Course (12 credits))

Administrator Håkan Hjalmarsson edited 23 October 2013

B23

 
Administrator Håkan Hjalmarsson created event 11 October 2013
Administrator Håkan Hjalmarsson edited 11 October 2013

Asymptotic Properties Consistency,   convergence and asymptotic normality of estimation methods.

Reading Material Ljung: Chapters 8,9

Optional references Slides 6,

Slides 7

Lecturer Cristian Rojas¶

Administrator Håkan Hjalmarsson edited 11 October 2013

Asymptotic Properties Consistency,   convergence and asymptotic normality of estimation methods.System Estimation Methods II: Structured Estimation Model structures, Maximum likelihood estimation, The prediction error method,  Instrumental variables

Reading Material Ljung: Chapters 8,9 7 (except 7.3)

Optional references
*
Slides 6, ¶ Slides 7¶ 1  
* Slides 2
* Slides 3
* Slides 4
* Slides 5
* Max-Max problem
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson edited 11 October 2013

System Estimation Methods II: Structured Estimation Model structures, Maximum likelihood estimation, The prediction error method,  Instrumental variables

Reading Material Ljung: Chapter 7 (except 7.3)

Optional referencesMaterial
* Slides 1  
* Slides 2
* Slides 3
* Slides 4
* Slides 5
* Max-Max problem
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson edited 11 October 2013

System Estimation Methods II: Structured Estimation Model structures, Maximum likelihood estimation, The prediction error method,  Instrumental variables

Reading Material Ljung: Chapter 7 (except 7.3)

Optional Material
* Slides 1  
* Slides 2
* Slides 3
* Slides 4
* Slides 5
* Max-Max problem
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson changed the permissions 14 October 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administrator Håkan Hjalmarsson edited 16 October 2013

System Estimation Methods III: Structured Estimation using Subspace identification Model structures, Maximum likelihood estimation, The prediction error method,  Instrumental variables

Reading Material Ljung: Chapter 7 (except 7.3)

Optional Material
* Slides 1  
* Slides 2
* Slides 3
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson edited 16 October 2013

System Estimation Methods III: Structured Estimation using Subspace identification Model structures, Maximum likelihood estimation, The prediction error method,  Instrumental variables

Reading Material Ljung: Chapter 7 (except 7.3)s  4.3, 7.3, 10.6

Optional Material
* Slides 1  
* Slides 2
* Slides 3
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson edited 16 October 2013

System Estimation Methods III: Structured Estimation using Subspace identification Model structures, Maximum likelihood estimation, The prediction error method,  Instrumental variables

Reading Material Ljung: Chapters  4.3, 7.3, 10.6

Optional Material
* Slides 1  
* Slides 2
* Slides 3
      4
Lecturer Cristian Rojas

Administrator Cristian Rojas edited 22 October 2013

System Estimation Methods III: Structured Estimation using Subspace identification Model structures, Maximum likelihood estimation, The prediction error method,  Instrumental variables

Reading Material Ljung: Chapters  4.3, 7.3, 10.6

Optional Material
* Slides      4
Lecturer Cristian Rojas

Administrator Håkan Hjalmarsson edited 23 October 2013

L31

Administrator Cristian Rojas edited 31 October 2013

System Estimation Methods III: Structured Estimation using Subspace identification Model structures, Maximum likelihood estimation, The prediction error method,  Instrumental variables

Reading Material Ljung: Chapters  4.3, 7.3, 10.6

Optional Material
* Slides 4
Lecturer Cristian Rojas

Administrator Cristian Rojas edited 31 October 2013

System Estimation Methods III: Structured Estimation using Subspace identification

Reading Material Ljung: Chapters  4.3, 7.3, 10.6

Optional Material
*
Lectur Slides 4
*  

Lecturer Cristian Rojas

Administrator Cristian Rojas edited 3 November 2013

System Estimation Methods III: Structured Estimation using Subspace identification

Reading Material Ljung: Chapters  4.3, 7.3, 10.6

Lectur Slides
*  Lecture 5
Lecturer Cristian Rojas

 
Administrator Håkan Hjalmarsson created event 11 October 2013
Administrator Håkan Hjalmarsson edited 11 October 2013

Parametric Estimation Methods Least squares, prediction error methods, instrumental variables and subspace methods.

Reading Material Ljung: Chapters 7, 10.6

Optional References Ljung, 1978

Slides      1,

Slides      2,

Slides      3,

Slides      4,

Slides      5,

Max-Max      problem,

Lecturer Håkan Hjalmarsson¶

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ParametricSystem Estimation  Methods Least squares, prediction error methods, instrumental variables and subspace methods.¶ Reading Material Ljung: Chapters 7, 10.6¶ Optional References Ljung, 1978¶ Slides      1,¶ Slides      2,¶ Slides      3, ¶ Slides      4, ¶ Slides      5,¶ Max-Max problem,I: Basic principles and unstructured estimation Basic principles for matching models and data, Non-parametric identification¶

Reading Material Ljung: Chapters 4.1-4.2, 4.5, , 6¶

Optional Material L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.

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System Estimation Methods II: Basic principles and unsStructured eEstimation Basic principles for matching models and data, Non-parametric identification

Reading Material Ljung: Chapters 4.1-4.2, 4.5, , 6

Optional Material L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.

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System Estimation Methods II: Structured Estimation Basic principles for matching models and data, Non-parametric identification¶
* Model structures
* The prediction error method
* The instrumental variables method
Reading Material Ljung: Chapters 4.1-4.2, 4.5, , 6

Optional Material L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.

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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.23, 47.5, , 6-7.7

Optional Material L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.

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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.3, 7.5-7.7

Optional Material L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.¶ Lecturer Håkan Hjalmarssonecturer Cristian Rojas

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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.3, 7.5-7.7

Optional Material
* Slides 1  
* Slides 2
Lecturer Cristian Rojas

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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.3, 7.5-7.7

Optional Material
* Slides 1  
* Slides 2
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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.3, 7.5-7.7

Optional MaterialLecture Slides
* Slides 2
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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.3, 7.5-7.7

Lecture Slides
* Slides 2
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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.3, 7.5-7.7

Lecture Slides
* Lecture 4
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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.3, 7.5-7.7

Lecture Slides
* Lecture 4
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System Estimation Methods II: Basic Principles cont'd, Structured Estimation
* Model structures
* The prediction error method
* The instrumental variables method
* Frequency domain identification
Reading Material Ljung: Chapters 4.1-4.3, 7.5-7.7

Lecture Slides
* Lecture 4
Optional Material J. C. Agüero, J. I. Yuz, G. C. Goodwin, R. A. Delgado, On the equivalence of time and frequency domain maximum likelihood estimation, Automatica, 46(2), 2010, 260-270.¶

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Models and Nonparametric  Methods Models of linear time invariant systems. Nonparametric time- and frequency-domain methods.

Reading material Ljung: Chapters 4, 6

Optional references Lemma 2.1

Ljung,1985

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Models and Nonparametric  Methods Models of linear time invariant systems. Nonparametric time- and frequency-domain methods.

Reading material Ljung: Chapters 4, 6

Optional references Lemma 2.1

Ljung,1985

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Models and Nonparametric  Methods Models of linear time invariSignal Estimation Methods Prediction, filtering antd systems. Nonparametric time- and frequency-domain methodsmoothing. Optimal  filtering. Orthogonality condition. Wiener/Kalman/particle filter.

Reading material Ljung: Chapters 4, 63

Optional references Lemma 2.1

Ljung,1985¶ Lecturer Håkan Hjalmarsson

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Signal Estimation Methods Prediction, filtering and smoothing. Optimal  filtering. Orthogonality condition. Wiener/Kalman/particle filter.

Reading material Ljung: Chapters 3

Optional references Lemma 2.1

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Signal Estimation Methods Prediction, filtering and smoothing. Optimal  filtering. Orthogonality condition. Wiener/Kalman/particle filter.

Reading material Ljung: Chapter 3

Optional referencesmaterial Lemma 2.1

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Signalystem Estimation Methods Prediction, filtering and smoothing. Optimal  filtering. Orthogonality condition. Wiener/Kalman/particle filterI: Basic principles and unstructured estimation
*
.

Reading material Ljung: Chapter 32.4, 6,

Optional material Lemma 2.1

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System Estimation Methods I: Basic principles and unstructured estimation
* .Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* .The maximum likelihood method

Reading material Ljung: Chapter 2.4, 6,

Optional material Lemma 2.1

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System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* .The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not 208-211), 7.4

Optional material Lemma 2.1

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System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* .The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material Lemma 2.1

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System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* .The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material Lemma 2.1. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.

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*
System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material
*
L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.

* Slides 1  
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* System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material
* L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.
* Slides 1  
* Lemma 2.1
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* System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material
* L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.
* Slides 1  
* Lemma 2.1
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Signals and Systems Linear time invariant systems and their uses (prediction, simulation).

Reading material Ljung: Chap 2,3

Optional references Lecturer Håkan Hjalmarsson¶

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Signals and Systems Linear time invariant systems and their uses (prediction, simulation).Stationary stochastic processes. Quasi-stationarity. Convergence properties

Reading material Ljung: Chap 2,3

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Signals Stationary stochastic processes. Quasi-stationarity. Convergence properties

Reading material Ljung: Chap 2,3

Optional references Lecturer Håkan Hjalmarsson

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Signals Stationary stochastic processes. Quasi-stationarity. Convergence properties

Reading material Ljung: Chap 2

Optional referencesmaterial -¶

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Signals Stationary stochastic processes. Quasi-stati Estimation Methods
* Minimum mean-square error estimation
* Prediction, filtering and smoothing.
* Optimal  filtering.
* Orthog
onarlity. Convergence propertie condition.
* Wiener/Kalman/particle filter
s

Reading material Ljung: Chap 2

Optional material -

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Signal Estimation Methods
* Minimum mean-square error estimation
* Prediction, filtering and smoothing.
* Optimal  filtering.
* Orthogonality condition.
* Wiener/Kalman/particle filters
Reading material Ljung: Chap 23

Optional material -

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Signal Estimation Methods
* Minimum mean-square error estimation
* Prediction, filtering and smoothing.
* Optimal  filtering.
* Orthogonality condition.
* Wiener/Kalman/particle filters
Reading material Ljung: Chapter 3

Optional material -

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Signal Estimation Methods
* Minimum mean-square error estimation
* Prediction, filtering and smoothing.
* Optimal  filtering.
* Wiener/Kalman/particle filters
Reading material Ljung: Chapter 3

Optional material -Lemma 2.1

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Signal Estimation Methods
* Minimum mean-square error estimation
* Prediction, filtering and smoothing.
* Optimal  filtering.
* Wiener/Kalman/particle filters
Reading material Ljung: Chapter 3

Optional material Lemma 2.1¶ Lecturer Håkan Hjalmarsson

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Introduction Presentation of basic ideas and concepts, review of probability and statistics.

Reading material Ljung: Chap 1, Apprndix. I, II

Optional references  Ljung,2010

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Introduction Presentation of basic ideas and concepts, review of probability . Linear time-invariandt statisticystems.

Reading material Ljung: Chap 1, Apprndix. I, IIters 1, 2.1, 2.2

Optional references  L. Ljung,. “Perspectives on    System Identification”. Annual    Reviews in Control, Vol. 34(1), pp. 1-12, 2010.

 Lecturer Håkan Hjalmarsson

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Introduction Presentation of basic ideas and concepts. Linear time-invariant systems.

Reading material Ljung: Chapters 1, 2.1, 2.2

Optional referencesmaterial  L. Ljung. “Perspectives on    System Identification”. Annual    Reviews in Control, Vol. 34(1), pp. 1-12, 2010.

 Lecturer Håkan Hjalmarsson

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Introduction Presentation of basic ideas and concepts. Linear time-invariant systems.

Reading material Ljung: Chapters 1, 2.1, 2.2

Optional material  L. Ljung. “Perspectives on    System Identification”. Annual    Reviews in Control, Vol. 34(1), pp. 1-12, 2010.

 Lecturer Håkan Hjalmarsson

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Introduction Presentation of basic ideas and concepts. Linear time-invariant systems.

Reading material Ljung: Chapters 1, 2.1, 2.2

Optional material  L. Ljung. “Perspectives on System Identification”. Annual    Reviews in Control, Vol. 34(1), pp. 1-12, 2010.

 Lecturer Håkan Hjalmarsson

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Introduction Presentation of b
* B
asic ideas and concepts. Linear time-invariant systems.¶
* Models of dynamical systems
* Noise models
* Stochastic processes
Reading material Ljung: Chapters 1, 2.1, 2.2

Optional material  L. Ljung. “Perspectives on System Identification”. Annual  Reviews in Control, Vol. 34(1), pp. 1-12, 2010.

 Lecturer Håkan Hjalmarsson

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Introduction
* Basic ideas and concepts.
* Models of dynamical systems
* Noise models
* Stochastic processes
Reading material Ljung: Chapters 1, 2.1, 2.2

Optional material  L. Ljung. “Perspectives on System Identification”. Annual  Reviews in Control, Vol. 34(1), pp. 1-12, 2010.

 Lecturer Håkan Hjalmarsson

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Lecture 143

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Model Selection and Model Validation
* Classic model order selection  criteria (AIC, BIC, MDL),
*  Hypothesis tests
Reading Material Ljung: Chapter 16¶

Optional References
* Slides 10
* Slides 11
Lecturer Cristian Rojas¶

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Model Selection and Model Validation
* Classic model order selection  criteria (AIC, BIC, MDL),
*  Hypothesis tests
Closed loop identification Ljung: Chapter 13.4-13.5¶

Reading Material Ljung: Chapter 16

Optional References
* Slides 10
* Slides 11
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Closed loop identification Ljung: Chapter 8.5, 13.4-13.5

Reading Material Ljung: Chapter 16

Optional References
* Slides 10
* Slides 11
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Closed loop identification Ljung: Chapter 8.5, 13.4-13.5

Reading Material Ljung: Chapter 16

Optional References
* Slides 10
* Slides 11
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Lecture 132

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Model Selection and Model Validation
* Classic model order selection  criteria (AIC, BIC, MDL),
*  Hypothesis tests
Reading Material Ljung: Chapter 16¶

Optional References
* Slides 10
* Slides 11
Lecturer Cristian Rojas¶

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Model Selection and Model VaOpen Loop Experiment Design
* Basics

* Informative experiments
* Choice of sampling interval and pre-sampling filters
* App
lidcation
* Classic model order selection  criteria (AIC, BIC, MDL),
*  Hypothesis tests
Reading Material Ljung: Chapter 16
s oriented experiment design 

* Relating application specifications to the identification specifications.
* Least-costly identification
* Computational issue

* Convexification
* Frequency-domain design
*  The cost of system and model complexity.
* Robust design
* Adaptive
Reading Material Ljung: Chapter 13.1-13.3, Applications oriented experiment design

Optional References
* Slides 10
* Slides 11
Lecturer Cristian Rojas

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Open Loop Experiment Design
* Basics

* Informative experiments
* Choice of sampling interval and pre-sampling filters
* Applications oriented experiment design 

* Relating application specifications to the identification specifications.
* Least-costly identification
* Computational issue

* Convexification
* Frequency-domain design
*  The cost of system and model complexity.
* Robust design
* Adaptive
Reading Material Ljung: Chapter 13.1-13.3, Applications oriented experiment design

Optional References
* Slides 10
* Slides 11
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Lecture 121

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Model Selection and Model Validation
* Classic model order selection  criteria (AIC, BIC, MDL),
*  Hypothesis tests
Reading Material Ljung: Chapter 16¶

Optional References
* Slides 10
* Slides 11
Lecturer Cristian Rojas¶

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Model Selection and Model ValidEstimation Algorithms
* Solving normal equations using QR-factoriz
ation
* Classic model order selection  criteria (AIC, BIC, MDL),
*  Hypothesis test
 Non-linear optimization
* Two- and multi-stage method
s
Reading Material Ljung: Chapter 160

Optional References
* Slides 10
* Slides 11
Lecturer Cristian Rojas

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Estimation Algorithms
* Solving normal equations using QR-factorization
*  Non-linear optimization
* Two- and multi-stage methods
* The EM-algorithm
Reading Material Ljung: Chapter 10

Optional References
* Slides 10
* Slides 11
Lecturer Cristian Rojas

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Estimation Algorithms
* Solving normal equations using QR-factorization
*  Non-linear optimization
* Two- and multi-stage methods
* The EM-algorithm
Reading Material Ljung: Chapter 10

Optional References
* Slides 10
* Slides 11
Lecturer Cristian RojasHåkan Hjalmarsson

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Estimation Algorithms
* Solving normal equations using QR-factorization
*  Non-linear optimization
* Two- and multi-stage methods
* The EM-algorithm
Reading Material Ljung: Chapter 10

Optional References
* Slides 10
* Slides 11
Lecturer Håkan Hjalmarsson

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Estimation Algorithms
* Solving normal equations using QR-factorization
*  Non-linear optimization
* Two- and multi-stage methods
* The EM-algorithm
Reading Material Ljung: Chapter 10

Optional References Lecturer Håkan HjalmarssonCristian Rojas

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Q11

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Lecture 110

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Estimation Algorithms
* Solving normal equations using QR-factorization
*  Non-linear optimization
* Two- and multi-stage methods
* The EM-algorithm
Reading Material Ljung: Chapter 10

Optional ReferencesLecture Slides
* Lecture 10¶


Lecturer Cristian Rojas

 
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