generalized linear models glms ii
play

Generalized Linear Models (GLMs) II Jonathan Pillow 1 <latexit - PowerPoint PPT Presentation

Oct 24, 2023 •151 likes •461 views

Statistical modeling and analysis of neural data NEU 560, Spring 2018 Lecture 10 Generalized Linear Models (GLMs) II Jonathan Pillow 1 <latexit

  1. Statistical modeling and analysis of neural data NEU 560, Spring 2018 Lecture 10 Generalized Linear Models (GLMs) II Jonathan Pillow 1

  2. <latexit sha1_base64="miR0nT8tJiZuVKiaUfnxELw/TEo=">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</latexit> <latexit sha1_base64="UNt4lEq3y3m8KLk2pIT2tsWp4c=">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</latexit> <latexit sha1_base64="HP+VfMA0E+XGBPwmKdZYtaDvzk=">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</latexit> Summary: 1. “Linear-Gaussian” GLM: Y | X, ~ k ∼ N ( X ~ k, � 2 I ) k = ( X T X ) − 1 X T Y ˆ 2. Bernoulli GLM: x t , ~ x t · ~ y t | ~ k ∼ Ber( f ( ~ k )) 3. Poisson GLM: 2

  3. Linear-Nonlinear-Poisson exponential Poisson stimulus filter nonlinearity spiking stimulus λ ( t ) k f conditional intensity (spike rate) 3

  4. <latexit sha1_base64="IqiPS5IzX5l+5dK3I8KUFXVZ9E=">AB7HicbVBNT8JAEJ3iF+IX6tHLRmLibRe1BvRi0dMLJBAQ7bLFla2Z3akIa/oMXD2q8+oO8+W9coAcFXzLJy3szmZkXplIYdN1vp7S2vrG5Vd6u7Ozu7R9UD49aJsk04z5LZKI7ITVcCsV9FCh5J9WcxqHk7XB8O/PbT1wbkagHnKQ8iOlQiUgwilZq9UYUybhfrbl1dw6ySryC1KBAs1/96g0SlsVcIZPUmK7nphjkVKNgk8rvczwlLIxHfKupYrG3AT5/NopObPKgESJtqWQzNXfEzmNjZnEoe2MKY7MsjcT/O6GUZXQS5UmiFXbLEoyiTBhMxeJwOhOUM5sYQyLeythI2opgxtQBUbgrf8irxL+rXde/erTVuijTKcAKncA4eXEID7qAJPjB4hGd4hTcncV6cd+dj0Vpyiplj+APn8weYqo6q</latexit> Fitting the nonlinearity Filter k specifies a direction in stimulus space 
 (i.e. a 1D subspace) 4

  5. <latexit sha1_base64="flj8ApT0XAd06blhKZPNAqW50=">ACDHicbZC9TsMwFIUdfkv5CzCyWBQkpiphAQakChbGIhFaqYkix3VaK4T2U6lKskLsPAqLAyAWHkANt4Gt80ALUey9Once3V9T5AyKpVlfRtLyura+u1jfrm1vbOrm3/yCTGDi4IQlohsgSRjlxFUMdJNBUFxwEgniG4m9c6ICEkTfq/GKfFiNOA0pBgpbfnmsTsiGZ+BK+gGwqEc3eIFIzKvChmVBSlbzaspjUVXAS7gao1PbNL7ef4CwmXGpOzZVq8HAlFMSNl3c0kSRGO0ID0NHIUE+nl02tKeKdPgwToR9XcOr+nshRLOU4DnRnjNRQztcm5n+1XqbCy+nPM0U4Xi2KMwYVAmcRAP7VBCs2FgDwoLqv0I8RDoTpQOs6xDs+ZMXwTlrXjbtO6vRuq7SqIFDcAROgQ3OQvcgjZwAaP4Bm8gjfjyXgx3o2PWeuSUc0cgD8yPn8AlRabeQ=</latexit> <latexit sha1_base64="O9GuRVuYx2v+9x/1eESUfYxnZko=">AB/XicbVC7TsMwFHXKq5RXADGxWFRITFXCAmwVLIxFIrRSE0WO47RWHTuynYoqsSvsDAYuU/2Pgb3DQDtBzpSsfn3Cvfe6KMUaUd59uqrayurW/UNxtb2zu7e/b+wYMSucTEw4IJ2YuQIoxy4mqGelkqA0YqQbjW5mfndMpKC3+tJRoIUDThNKEbaSKF95I8Jho/Qx7HQsHzk4Si0m07LKQGXiVuRJqjQCe0vPxY4TwnXmCGl+q6T6aBAUlPMyLTh54pkCI/QgPQN5SglKijK9afw1CgxTIQ0xTUs1d8TBUqVmqSR6UyRHqpFbyb+5/VznVwGBeVZrgnH84+SnEt4CwLGFNJsGYTQxCW1OwK8RBJhLVJrGFCcBdPXibeuq5d45zfZ1lUYdHIMTcAZcAHa4BZ0gAcwKMAzeAVv1pP1Yr1bH/PWmlXNHI/sD5/AIQHlL4=</latexit> Fitting the nonlinearity 1) Project onto subspace spanned by k 5

  6. <latexit sha1_base64="flj8ApT0XAd06blhKZPNAqW50=">ACDHicbZC9TsMwFIUdfkv5CzCyWBQkpiphAQakChbGIhFaqYkix3VaK4T2U6lKskLsPAqLAyAWHkANt4Gt80ALUey9Once3V9T5AyKpVlfRtLyura+u1jfrm1vbOrm3/yCTGDi4IQlohsgSRjlxFUMdJNBUFxwEgniG4m9c6ICEkTfq/GKfFiNOA0pBgpbfnmsTsiGZ+BK+gGwqEc3eIFIzKvChmVBSlbzaspjUVXAS7gao1PbNL7ef4CwmXGpOzZVq8HAlFMSNl3c0kSRGO0ID0NHIUE+nl02tKeKdPgwToR9XcOr+nshRLOU4DnRnjNRQztcm5n+1XqbCy+nPM0U4Xi2KMwYVAmcRAP7VBCs2FgDwoLqv0I8RDoTpQOs6xDs+ZMXwTlrXjbtO6vRuq7SqIFDcAROgQ3OQvcgjZwAaP4Bm8gjfjyXgx3o2PWeuSUc0cgD8yPn8AlRabeQ=</latexit> <latexit sha1_base64="O9GuRVuYx2v+9x/1eESUfYxnZko=">AB/XicbVC7TsMwFHXKq5RXADGxWFRITFXCAmwVLIxFIrRSE0WO47RWHTuynYoqsSvsDAYuU/2Pgb3DQDtBzpSsfn3Cvfe6KMUaUd59uqrayurW/UNxtb2zu7e/b+wYMSucTEw4IJ2YuQIoxy4mqGelkqA0YqQbjW5mfndMpKC3+tJRoIUDThNKEbaSKF95I8Jho/Qx7HQsHzk4Si0m07LKQGXiVuRJqjQCe0vPxY4TwnXmCGl+q6T6aBAUlPMyLTh54pkCI/QgPQN5SglKijK9afw1CgxTIQ0xTUs1d8TBUqVmqSR6UyRHqpFbyb+5/VznVwGBeVZrgnH84+SnEt4CwLGFNJsGYTQxCW1OwK8RBJhLVJrGFCcBdPXibeuq5d45zfZ1lUYdHIMTcAZcAHa4BZ0gAcwKMAzeAVv1pP1Yr1bH/PWmlXNHI/sD5/AIQHlL4=</latexit> Fitting the nonlinearity 1) Project onto subspace spanned by k 2) take histogram of projected stimuli projection onto u k 6

  7. <latexit sha1_base64="flj8ApT0XAd06blhKZPNAqW50=">ACDHicbZC9TsMwFIUdfkv5CzCyWBQkpiphAQakChbGIhFaqYkix3VaK4T2U6lKskLsPAqLAyAWHkANt4Gt80ALUey9Once3V9T5AyKpVlfRtLyura+u1jfrm1vbOrm3/yCTGDi4IQlohsgSRjlxFUMdJNBUFxwEgniG4m9c6ICEkTfq/GKfFiNOA0pBgpbfnmsTsiGZ+BK+gGwqEc3eIFIzKvChmVBSlbzaspjUVXAS7gao1PbNL7ef4CwmXGpOzZVq8HAlFMSNl3c0kSRGO0ID0NHIUE+nl02tKeKdPgwToR9XcOr+nshRLOU4DnRnjNRQztcm5n+1XqbCy+nPM0U4Xi2KMwYVAmcRAP7VBCs2FgDwoLqv0I8RDoTpQOs6xDs+ZMXwTlrXjbtO6vRuq7SqIFDcAROgQ3OQvcgjZwAaP4Bm8gjfjyXgx3o2PWeuSUc0cgD8yPn8AlRabeQ=</latexit> <latexit sha1_base64="O9GuRVuYx2v+9x/1eESUfYxnZko=">AB/XicbVC7TsMwFHXKq5RXADGxWFRITFXCAmwVLIxFIrRSE0WO47RWHTuynYoqsSvsDAYuU/2Pgb3DQDtBzpSsfn3Cvfe6KMUaUd59uqrayurW/UNxtb2zu7e/b+wYMSucTEw4IJ2YuQIoxy4mqGelkqA0YqQbjW5mfndMpKC3+tJRoIUDThNKEbaSKF95I8Jho/Qx7HQsHzk4Si0m07LKQGXiVuRJqjQCe0vPxY4TwnXmCGl+q6T6aBAUlPMyLTh54pkCI/QgPQN5SglKijK9afw1CgxTIQ0xTUs1d8TBUqVmqSR6UyRHqpFbyb+5/VznVwGBeVZrgnH84+SnEt4CwLGFNJsGYTQxCW1OwK8RBJhLVJrGFCcBdPXibeuq5d45zfZ1lUYdHIMTcAZcAHa4BZ0gAcwKMAzeAVv1pP1Yr1bH/PWmlXNHI/sD5/AIQHlL4=</latexit> Fitting the nonlinearity 1) Project onto subspace spanned by k 2) take histogram of projected stimuli STA response 3) ML estimate of Poisson rate in each bin is # spikes / # stimuli projection onto u k 7

Recommend

Generalized Linear Models (GLMs) Jonathan Pillow 1 Example 3: unknown neuron 100 75 (spike

Generalized Linear Models (GLMs) Jonathan Pillow 1 Example 3: unknown neuron 100 75 (spike

Statistical modeling and analysis of neural data NEU 560, Spring 2018 Lecture 9 Generalized Linear Models (GLMs) Jonathan Pillow 1 Example 3: unknown neuron 100 75 (spike count) 50 25 0 -25 0 25 (contrast) Be the computational

306 views • 27 slides
Generalized Linear Models (GLMs/GLIMs) STAT 757 Tuesday, April 19, 2016 Model Framework The GLM

Generalized Linear Models (GLMs/GLIMs) STAT 757 Tuesday, April 19, 2016 Model Framework The GLM

Generalized Linear Models (GLMs/GLIMs) STAT 757 Tuesday, April 19, 2016 Model Framework The GLM is described by three components: 1. The random component specifies the conditional distribution of y i | x i and is typically a member of the

382 views • 10 slides
Statistics for Applications Chapter 10: Generalized Linear Models (GLMs) 1/52 Linear model A

Statistics for Applications Chapter 10: Generalized Linear Models (GLMs) 1/52 Linear model A

Statistics for Applications Chapter 10: Generalized Linear Models (GLMs) 1/52 Linear model A linear model assumes Y | X N ( ( X ) , 2 I ) , And E( Y | X ) = ( X ) = X , I 2/52 Components of a linear model The two components

719 views • 53 slides
Advanced Section #5: Generalized Linear Models: Logistic Regression and Beyond Marios Mattheakis

Advanced Section #5: Generalized Linear Models: Logistic Regression and Beyond Marios Mattheakis

Advanced Section #5: Generalized Linear Models: Logistic Regression and Beyond Marios Mattheakis and Pavlos Protopapas CS109A Introduction to Data Science Pavlos Protopapas and Kevin Rader 1 Outline 1. Generalized Linear Models (GLMs): a.

961 views • 37 slides
Workshop 3 Building from Linear Models to Generalised Linear Models Part 2: GLMs 2 2 What are

Workshop 3 Building from Linear Models to Generalised Linear Models Part 2: GLMs 2 2 What are

1 Workshop 3 Building from Linear Models to Generalised Linear Models Part 2: GLMs 2 2 What are linear models? Stage 1: response continuous - General Linear Model Procedure Response Predictors Single linear regression y ~ x Continuous

399 views • 19 slides
Going be y ond linear regression G E N E R AL IZE D L IN E AR MOD E L S IN P YTH ON Ita Ciro v

Going be y ond linear regression G E N E R AL IZE D L IN E AR MOD E L S IN P YTH ON Ita Ciro v

Going be y ond linear regression G E N E R AL IZE D L IN E AR MOD E L S IN P YTH ON Ita Ciro v ic Done v Data Science Cons u ltant Co u rse objecti v es Learn b u ilding blocks of GLMs Chapter 1: Ho w are GLMs an e x tension of linear models

577 views • 42 slides
Limitations of linear models Richard Erickson Instructor DataCamp Generalized Linear Models in

Limitations of linear models Richard Erickson Instructor DataCamp Generalized Linear Models in

DataCamp Generalized Linear Models in R GENERALIZED LINEAR MODELS IN R Limitations of linear models Richard Erickson Instructor DataCamp Generalized Linear Models in R Course overview Chapter 1: Review and limits of linear model and Poisson

1.05k views • 35 slides
Introduction to General and Generalized Linear Models Generalized Linear Models - part II Henrik

Introduction to General and Generalized Linear Models Generalized Linear Models - part II Henrik

Introduction to General and Generalized Linear Models Generalized Linear Models - part II Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby October 2010 Henrik Madsen Poul

638 views • 29 slides
Introduction to General and Generalized Linear Models Generalized Linear Models - part I Henrik

Introduction to General and Generalized Linear Models Generalized Linear Models - part I Henrik

Introduction to General and Generalized Linear Models Generalized Linear Models - part I Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby October 2010 Henrik Madsen Poul

740 views • 32 slides
Introduction to General and Generalized Linear Models Generalized Linear Models - part III Henrik

Introduction to General and Generalized Linear Models Generalized Linear Models - part III Henrik

Introduction to General and Generalized Linear Models Generalized Linear Models - part III Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby October 2010 Henrik Madsen Poul

544 views • 22 slides
Introduction to General and Generalized Linear Models General Linear Models - part I Henrik

Introduction to General and Generalized Linear Models General Linear Models - part I Henrik

Introduction to General and Generalized Linear Models General Linear Models - part I Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby October 2010 Henrik MadsenPoul

922 views • 37 slides
Linear Models are Most Favorable among Generalized Linear Models Kuan-Yun Lee and Thomas A.

Linear Models are Most Favorable among Generalized Linear Models Kuan-Yun Lee and Thomas A.

Linear Models are Most Favorable among Generalized Linear Models Kuan-Yun Lee and Thomas A. Courtade Electrical Engineering and Computer Sciences University of California, Berkeley ISIT 2020 K.-Y. Lee and T. A. Courtade (Berkeley) New Minimax

892 views • 44 slides
Proper Generalized Decomposition for Linear and Non-Linear Stochastic Models Olivier Le Matre 1

Proper Generalized Decomposition for Linear and Non-Linear Stochastic Models Olivier Le Matre 1

Motivation Proper Generalized Decomposition Nonlinear problems : N-S Conclusion &amp; closing remarks Proper Generalized Decomposition for Linear and Non-Linear Stochastic Models Olivier Le Matre 1 Lorenzo Tamellini 2 and Anthony Nouy 3 1

960 views • 60 slides
Introduction to General and Generalized Linear Models General Linear Models - part II Henrik

Introduction to General and Generalized Linear Models General Linear Models - part II Henrik

Introduction to General and Generalized Linear Models General Linear Models - part II Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby October 2010 Henrik Madsen Poul

912 views • 32 slides
Generalized Nonlinear Models gnm : a Package for Generalized Nonlinear Models Same form as

Generalized Nonlinear Models gnm : a Package for Generalized Nonlinear Models Same form as

Generalized Nonlinear Models gnm : a Package for Generalized Nonlinear Models Same form as generalized linear models: g ( E ( Y )) = g ( ) = ( x ; ) Var ( Y ) = f ( ) Heather Turner and David Firth except ( x ; ) can be

543 views • 4 slides
Generalized linear models Sren Hjsgaard Department of Mathematical Sciences Aalborg

Generalized linear models Sren Hjsgaard Department of Mathematical Sciences Aalborg

Generalized linear models Sren Hjsgaard Department of Mathematical Sciences Aalborg University, Denmark October 29, 2012 Printed: October 29, 2012 File: glim-slides.tex 2 Contents 3 1 Densities for generalized linear models Consider

380 views • 20 slides
Overview of logistic regression Richard Erickson Instructor DataCamp Generalized Linear Models

Overview of logistic regression Richard Erickson Instructor DataCamp Generalized Linear Models

DataCamp Generalized Linear Models in R GENERALIZED LINEAR MODELS IN R Overview of logistic regression Richard Erickson Instructor DataCamp Generalized Linear Models in R DataCamp Generalized Linear Models in R DataCamp Generalized Linear

802 views • 32 slides
Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan February 7, 2017

Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan February 7, 2017

-1- Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan February 7, 2017 Table of contents 1 Generalized Linear Mixed Effects Models 1 2 Worked Examples 5 1. Generalized Linear Mixed Effects Models 1.1.

286 views • 5 slides
Generalized linear mixed effects models Consider stochastic vector Y = ( Y 1 , . . . , Y n ) and

Generalized linear mixed effects models Consider stochastic vector Y = ( Y 1 , . . . , Y n ) and

Generalized linear mixed effects models Consider stochastic vector Y = ( Y 1 , . . . , Y n ) and vector of random Generalized linear mixed models - computation of effects U = ( U 1 , . . . , U m ). the likelihood function Two step formulation of

169 views • 6 slides
Multiple logistic regression Richard Erickson Instructor DataCamp Generalized Linear Models in

Multiple logistic regression Richard Erickson Instructor DataCamp Generalized Linear Models in

DataCamp Generalized Linear Models in R GENERALIZED LINEAR MODELS IN R Multiple logistic regression Richard Erickson Instructor DataCamp Generalized Linear Models in R Chapter overview Multiple logistic regression Formulas in R Model

759 views • 32 slides
Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan 07 Feb 2017

Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan 07 Feb 2017

Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan 07 Feb 2017 Section 1 Generalized Linear Mixed Effects Models Parameter Estimation lm LME (integrate likelihood across all unobserved levels random

414 views • 23 slides
Generalized Linear Model Certain nonlinear models with a specific structure arise from using

Generalized Linear Model Certain nonlinear models with a specific structure arise from using

ST 762 Nonlinear Statistical Models for Univariate and Multivariate Response Generalized Linear Model Certain nonlinear models with a specific structure arise from using linear modeling with a parent distribution in the exponential family. If

640 views • 10 slides
LOGISTIC REGRESSION AND GENERALIZED LINEAR MODELS W. RYAN LEE CS109/AC209/STAT121 ADVANCED

LOGISTIC REGRESSION AND GENERALIZED LINEAR MODELS W. RYAN LEE CS109/AC209/STAT121 ADVANCED

LOGISTIC REGRESSION AND GENERALIZED LINEAR MODELS W. RYAN LEE CS109/AC209/STAT121 ADVANCED SECTION INSTRUCTORS: P. PROTOPAPAS, K. RADER FALL 2017, HARVARD UNIVERSITY In this section, we introduce the idea and theory of generalized linear

546 views • 7 slides
Model Assessment Generalized Linear Models Marco Chiarandini Department of Mathematics &amp;

Model Assessment Generalized Linear Models Marco Chiarandini Department of Mathematics &amp;

DM825 Introduction to Machine Learning Lecture 4 Model Assessment Generalized Linear Models Marco Chiarandini Department of Mathematics &amp; Computer Science University of Southern Denmark Error Estimation Methods Outline Generalized

437 views • 24 slides

More recommend