R - Hierarchical Models Examples
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This example video covers how to perform a first order CFA, second order CFA, and bi-factor CFA. Lavaan, semPath, and the cfa functions are covered, along with interpretation of the models and some guidance on how to pi
From playlist Structural Equation Modeling
In this video we cover the idea of marginal cost. This is simply the derivative of the cost function. We can roughly define marginal cost as the cost of producing one additional item. For more videos please visit http://www.mysecretmathtutor.com
From playlist Calculus
R - Hierarchical Confirmatory Factor Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers the basics to understanding a hierarchical CFA, in contrast to a bifactor CFA model. Interpretation and discussion of the theoretical differences between these models and first order models are discu
From playlist Structural Equation Modeling
R - Structural Equation Model Basics Lecture 1
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers the basic terminology for structural equation modeling including: identification, scaling, variable types, manifest/latent variables, path coefficient types, endogenous/exogenous variables, degrees o
From playlist Structural Equation Modeling
From playlist Courses and Series
R - Full Structural Equation Models Lecture
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers how to program reflexive and formative indicators in lavaan, their interpretation, and how to use them. We talk about how to convert from a measurement model only to a full structural model. Lectur
From playlist Structural Equation Modeling
The Benefits of Functional Architectures | Systems Engineering, Part 3
See the other videos in this series: https://www.youtube.com/playlist?list=PLn8PRpmsu08owzDpgnQr7vo2O-FUQm_fL Functional, logical, and physical architectures are important tools for designing complex systems. We describe what architectures are and how they contribute to the early stages of
From playlist Systems Engineering
From playlist Courses and Series
Marginal-based Methods for Differentially Private Synthetic Data
A Google TechTalk, presented by Ryan McKenna, 2021/12/08 Differential Privacy for ML series.
From playlist Differential Privacy for ML
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
Pavel Krupskiy - Conditional Normal Extreme-Value Copulas.
Dr Pavel Krupskiy (University of Melbourne) presents “Conditional Normal Extreme-Value Copulas”, 14 August 2020. Seminar organised by UNSW Sydney.
From playlist Statistics Across Campuses
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 (10): Structured Prediction
This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * Teacher Forcing and Exposure Bias * Local vs. Global, Label Bias * The Structured Perceptron * Structured Max-margin Objectives Slides: http://phontron.com/class/nn4nlp2017/assets/slides/nn4n
From playlist CMU Neural Nets for NLP 2017
Håvard Rue: Bayesian computation with INLA
Abstract: This talk focuses on the estimation of the distribution of unobserved nodes in large random graphs from the observation of very few edges. These graphs naturally model tournaments involving a large number of players (the nodes) where the ability to win of each player is unknown.
From playlist Probability and Statistics
Alan Yuille: "Learning Discriminative Models of Objects and Images"
Graduate Summer School 2012: Deep Learning, Feature Learning "Learning Discriminative Models of Objects and Images" Alan Yuille, UCLA Institute for Pure and Applied Mathematics, UCLA July 13, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-sc
From playlist GSS2012: Deep Learning, Feature Learning
Catherine Calder - Spatial Confounding and Restricted Spatial Regression Methods
Professor Catherine Calder (University of Texas at Austin) presents “Spatial Confounding and Restricted Spatial Regression Methods”, 18 June 2021.
From playlist Statistics Across Campuses
Tobias Fritz : The inflation technique for casual inference with hidden variable
Recording during the thematic meeting : "Geometrical and Topological Structures of Information" the September 01, 2017 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent
From playlist Geometry
Linh Nghiem - Estimation of continuous non-Gaussian graphical models
Dr Linh Nghiem (ANU) presents "Estimation of continuous non-Gaussian graphical models", 26 June 2020.
From playlist Statistics Across Campuses
QRM 10-3: The Model Building Approach
This video is taken from by basic RM course and deals with MR under the model-building approach.
From playlist Quantitative Risk Management
Statistics / Data Analysis (Lecture 1) by B. Wandelt
Program Cosmology - The Next Decade ORGANIZERS : Rishi Khatri, Subha Majumdar and Aseem Paranjape DATE : 03 January 2019 to 25 January 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore The great observational progress in cosmology has revealed some very intriguing puzzles, the most i
From playlist Cosmology - The Next Decade