Latent Growth Curve Modeling | Part 2 | Structural Equation Modeling
In the second installment of this video series, I will discuss the essential concepts in Growth Curve Modeling within the Structural Equation Modeling framework.
From playlist Growth Curve Models
R - Latent Growth Models Lecture
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This video is a lecture that covers latent growth (curve) modeling - including the steps for random intercepts and slopes taken from the Beaujean SEM lavaan book. Lecture materials and assignment available at statistic
From playlist Structural Equation Modeling
Latent Growth Curve Modeling | Part 1
In the second installment of this video series, I will discuss the essential concepts in Growth Curve Modeling.
From playlist Growth Curve Models
R - Latent Growth (Curve) Example
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This video covers an example of how to perform a latent growth model with steps over intercepts, random intercepts, random slopes, slopes, covariance, and residuals. Lavaan and the growth() functions are used. Lecture
From playlist Structural Equation Modeling
R - Latent Growth Models Lecture
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom In this section, you will learn about latent growth models and how to analyze them in a similar fashion to multilevel models. You can learn more at: https://statisticsofdoom.com/page/structural-equation
From playlist Structural Equation Modeling 2020
R - SEM - Latent (Growth) Curve Modeling Class Assignment
Recorded: Summer 2015 Lecturer: Dr. Erin M. Buchanan Packages needed: lavaan, semPlot Class assignment for structural equation modeling. Topic covers how program different types of latent curve models (linear only) including fit indices, random slopes and intercepts, and their interpretat
From playlist Structural Equation Modeling
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.
generative model vs discriminative model
understanding difference between generative model and discriminative model with simple example. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
Topographic VAEs learn Equivariant Capsules (Machine Learning Research Paper Explained)
#tvae #topographic #equivariant Variational Autoencoders model the latent space as a set of independent Gaussian random variables, which the decoder maps to a data distribution. However, this independence is not always desired, for example when dealing with video sequences, we know that s
From playlist Papers Explained
Marc'Aurelio Ranzato: "Deep Gated MRFs, Pt. 1"
Graduate Summer School 2012: Deep Learning, Feature Learning "Deep Gated MRFs, Pt. 1" Marc'Aurelio Ranzato, Google Inc. Institute for Pure and Applied Mathematics, UCLA July 23, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-lear
From playlist GSS2012: Deep Learning, Feature Learning
R - Confirmatory Factor Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom This video covers the basics of confirmatory factor analysis or measurement models. You will learn about how to build, analyze, summarize, and diagram a measurement model in lavan. You can learn more at:
From playlist Structural Equation Modeling 2020
R - Hierarchical Confirmatory Factor Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom This video covers the second round of confirmatory factor analysis or measurement models. You will learn how to create a hierarchical model and a bifactor CFA model, along with the special considerations
From playlist Structural Equation Modeling 2020
R - Full Structural Equation Models Lecture
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom This video covers the third round of confirmatory factor analysis or measurement models. In this last section, you will learn how to create a fully latent or full structural equation model. You can lear
From playlist Structural Equation Modeling 2020
DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models
This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent
From playlist Learning resources
CMU Neural Nets for NLP 2017 (15): Latent Variable Models
This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * Generative vs. Discriminative, Deterministic vs. Random Variables * Variational Autoencoders * Handling Discrete Latent Variables * Examples of Variational Autoencoders in NLP Slides: http://
From playlist CMU Neural Nets for NLP 2017
R - Multitrait Multimethod (MTMM) Lecture
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom This video covers multi trait multi method analyses, which can be used to understand the convergent and divergent validity of scales. You will learn how to program and examine the steps to these models us
From playlist Structural Equation Modeling 2020
Lecturer: Dr. Erin M. Buchanan Fall 2020 https://www.patreon.com/statisticsofdoom This video is part of my structural equation modeling class - you will learn about SEM terminology, degrees of freedom, specification, and start to see some lavaan output. You can learn more at: https://
From playlist Structural Equation Modeling 2020
Ruslan Salakhutdinov: "Learning Hierarchical Generative Models, Pt. 1"
Graduate Summer School 2012: Deep Learning, Feature Learning "Learning Hierarchical Generative Models, Pt. 1" Ruslan Salakhutdinov, University of Toronto Institute for Pure and Applied Mathematics, UCLA July 23, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-school
From playlist GSS2012: Deep Learning, Feature Learning