Mod-01 Lec-01 Introduction to multivariate statistical modeling
Applied Multivariate Statistical Modeling by Dr J Maiti,Department of Management, IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in.
nptelhrd
Expectations and variance of a random vector - part 4
This video explains what is meant by the expectations and variance of a vector of random variables. Check out ...
Ben Lambert
Discrete vs Continuous Biomechanical Data Analysis - Todd Pataky
Lecture 22 of the Sports Biomechanics Lecture Series #SportsBiomLS Todd Pataky presents a comparison and discussion of discrete (e.g. maximum or ...
Stuart McErlain-Naylor
Mod-03 Lec-20 Multivariate Analysis - V
Statistical Methods for Scientists and Engineers by Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur For more details on NPTEL visit ...
nptelhrd
"Univariate versus multivariate methods for lesion-symptom mapping", Maria Ivanova
Lecture in the C-STAR series, by Maria Ivanova, PhD (University of California, Berkeley), on May 21st, 2020. Website: https://aphasia.berkeley.edu Full title: ...
C-STAR Lecture Series
Multivariate Statistical Analysis Part I: Introduction and Mean Comparison (with R demonstration)
For this seminar, I will take you through a general introduction of multivariate analysis and perform an R demonstration of a simple multivariate analysis: mean ...
RenaissanceWoman
Bayesian inference for multivariate Gaussians
Dirk Ostwald
Mod-03 Lec-22 Multivariate Analysis - VII
Statistical Methods for Scientists and Engineers by Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur For more details on NPTEL visit ...
nptelhrd
4. Parametric Inference (cont.) and Maximum Likelihood Estimation
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet ...
MIT OpenCourseWare
NeuroHackademy: Jeanette Mumford - The difference between prediction and explanation
As a part of NeuroHackademy 2020, Jeanette Mumford (U of Wisconsin-Madison) gives a lecture on "The difference between prediction and explanation.
UW eScience Institute
Maximum likelihood: Normal error distribution - estimator variance part 3
This video works through for the estimated asymptotic variance of Maximum Likelihood estimators of the mean and variance, in a standard normally distributed ...
Ben Lambert
75 Days CSIR-UGC NET Crash Course | Multivariate Normal Distribution | Unacademy Live CSIR UGC NET
In this class we are going to discuss Some important Questions and related Theory of Multivariate Analysis. Upcoming Free Classes: ...
Unacademy Live - CSIR UGC NET
Mod-03 Lec-16 Multivariate Analysis - I
Statistical Methods for Scientists and Engineers by Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur For more details on NPTEL visit ...
nptelhrd
PS3 : multivariate distributions, marginals, conditionals, bayes theorem, independence
Swollen Calf
Claire Monteleoni: Deep Unsupervised Learning for Climate Informatics
This is the live stream of the "Machine Learning in Science" Conference 2020. The conference is held by the Cluster of Excellence "Machine Learning: New ...
Tübingen Machine Learning
Joseph Salmon: The smoothed multivariate square-root Lasso: an optimization lens on concomitant...
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 04, 2020 by the Centre International de ...
Centre International de Rencontres Mathématiques
PyData Tel Aviv Meetup: Introduction to Causal Inference in Time Series Data - Shay Palachy
PyData Tel Aviv Meetup #28 2 January 2020 Sponsored and Hosted by PayPal https://www.meetup.com/PyData-Tel-Aviv/ In this talk I will give concise review of ...
PyData
Multivariate Data Analysis
Marketing Management-I
Steps for simulating multivariate normal data in R
This video demonstrates a set of steps for simulating multivariate normal data using R. Three packages are used in the demonstration: 'JWileymisc', 'MASS', and ...
Mike Crowson
Bayesian Deep Learning and Probabilistic Model Construction - ICML 2020 Tutorial
Bayesian Deep Learning and a Probabilistic Perspective of Model Construction ICML 2020 Tutorial Bayesian inference is especially compelling for deep neural ...
Andrew Gordon Wilson
Mod-03 Lec-19 Multivariate Analysis - IV
Statistical Methods for Scientists and Engineers by Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur For more details on NPTEL visit ...
nptelhrd
Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series
The Turing Lectures - Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series. Click the below timestamps to navigate the ...
The Alan Turing Institute
Opinionated Lessons in Statistics: #17 The Multivariate Normal Distribution
17th segment in the Opinionated Lessons in Statistics series of webcasts, based on a course given at the University of Texas at Austin by Professor William H.
opinionatedlessons
Lecture 17 - Covariance, Correlation, Multivariate Distributions
This is lecture 17 in BIOS 660 (Probability and Statistical Inference I) at UNC-Chapel Hill for fall of 2014.
Eric Bair
undergraduate machine learning 18: Least squares and the multivariate Gaussian
Least squares and the multivariate Gaussian. The slides are available here: http://www.cs.ubc.ca/~nando/340-2012/lectures.php This course was taught in 2012 ...
Nando de Freitas
6. Maximum Likelihood Estimation (cont.) and the Method of Moments
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet ...
MIT OpenCourseWare
fMRI Bootcamp Part 4 - Multivariate Analysis
Rebecca Saxe - MIT.
MITCBMM
Deciphering Cancer Genomes and Networks
Deciphering Cancer Genomes and Networks Air date: Wednesday, February 5, 2020, 3:00:00 PM Category: WALS - Wednesday Afternoon Lectures Runtime: ...
NIH VideoCast
1 - Marginal probability for continuous variables
This explains what is meant by a marginal probability for continuous random variables, how to calculate marginal probabilities and the graphical intuition behind ...
Ox educ
Fast Quantification of Uncertainty and Robustness with Variational Bayes
In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. These choices may be somewhat subjective and reasonably ...
Microsoft Research
Maximum likelihood: Normal error distribution - estimator variance part 1
This video works through for the estimated asymptotic variance of Maximum Likelihood estimators of the mean and variance, in a standard normally distributed ...
Ben Lambert
Conjugate Prior for Variance of Normal Distribution with known mean
This is a demonstration of how to show that an Inverse Gamma distribution is the conjugate prior for the variance of a normal distribution with known mean.
deetoher
Growth Curve Episode 4: A Structural Equation Modeling Framework
In a prior episode, Patrick explored how growth models can be estimated within a multilevel linear modeling framework. In this episode he discusses how growth ...
Curran-Bauer Analytics
Mod-01 Lec-03 Univariate descriptive statistics
Applied Multivariate Statistical Modeling by Dr J Maiti,Department of Management, IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in.
nptelhrd
Deep Generative models and Inverse Problems - Alexandros Dimakis
Seminar on Theoretical Machine Learning Topic:Deep Generative models and Inverse Problems Speaker: Alexandros Dimakis Affiliation: University of Texas at ...
Institute for Advanced Study
Statistical Inference for Analysis of Massive Health Data: Challenges and Opportunities
Speaker: Professor Xihong Lin - Chair, Department of Biostatistics, Harvard T.H. Chan School of Public Health Massive data from genome, exposome, and ...
RoyalStatSoc
02417 Lecture 10 part B: Parameter estimation in multivariate ARMA models
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ...
Lasse Engbo Christiansen
RSS Discussion Meeting: Functional models for time-varying random objects
Functional data analysis provides a popular toolbox of functional models for the analysis of samples of random functions that are real valued. In recent years ...
RoyalStatSoc
Hypothesis Testing (FRM Part 1 2020 – Book 2 – Chapter 6)
AnalystPrep's FRM Part 1 Video Series For FRM Part 1 Study Notes, Practice Questions, and Mock Exams Register an Account at https://analystprep.com/frm/ ...
AnalystPrep
TUTORIAL: Recent Development in Selective Inference II
Adaptive Data Analysis Workshop Snigdha Panigrahi https://simons.berkeley.edu/talks/tutorial-recent-development-selective-inference-ii.
Simons Institute
David Dunson: Scalable Bayesian Inference (NeurIPS 2018 Tutorial)
Abstract: This tutorial will provide a practical overview of state-of-the-art approaches for analyzing massive data sets using Bayesian statistical methods. The first ...
Steven Van Vaerenbergh
Mod-03 Lec-03 Basic Concepts of Point Estimations - II
Statistical Inference by Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in.
nptelhrd
