(ML 7.7.A1) Dirichlet distribution
Definition of the Dirichlet distribution, what it looks like, intuition for what the parameters control, and some statistics: mean, mode, and variance.
From playlist Machine Learning
(ML 7.8) Dirichlet-Categorical model (part 2)
The Dirichlet distribution is a conjugate prior for the Categorical distribution (i.e. a PMF a finite set). We derive the posterior distribution and the (posterior) predictive distribution under this model.
From playlist Machine Learning
(ML 7.7) Dirichlet-Categorical model (part 1)
The Dirichlet distribution is a conjugate prior for the Categorical distribution (i.e. a PMF a finite set). We derive the posterior distribution and the (posterior) predictive distribution under this model.
From playlist Machine Learning
Value distribution of long Dirichlet polynomials and applications to the Riemann...-Maksym Radziwill
Maksym Radziwill Value distribution of long Dirichlet polynomials and applications to the Riemann zeta-function Stanford University; Member, School of Mathematics October 1, 2013 For more videos, visit http://video.ias.edu
From playlist Mathematics
Multivariate Gaussian distributions
Properties of the multivariate Gaussian probability distribution
From playlist cs273a
Continuous Distributions: Beta and Dirichlet Distributions
Video Lecture from the course INST 414: Advanced Data Science at UMD's iSchool. Full course information here: http://www.umiacs.umd.edu/~jbg/teaching/INST_414/
From playlist Advanced Data Science
Functional Analysis Lecture 16 2014 03 25 Tempered Distributions
Recall definition of tempered distributions. Elementary properties. Continuity of tempered distribution with respect to a single Schwartz class norm; extension of compactly supported distributions to tempered distributions. Construction of tempered distributions from functions. Derivat
From playlist Course 9: Basic Functional and Harmonic Analysis
Topic Models: Variational Inference for Latent Dirichlet Allocation (with Xanda Schofield)
This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://sites.google.com/umd.edu/2021cl1webpage/ (Including homeworks and reading.) Xanda's Webpage: https://www.cs.hmc.edu/~xanda
From playlist Computational Linguistics I
From playlist Contributed talks One World Symposium 2020
The Normal Distribution (1 of 3: Introductory definition)
More resources available at www.misterwootube.com
From playlist The Normal Distribution
Daniel Slonim (Purdue) -- Random Walks in Dirichlet Random Environments on Z with Bounded Jumps
Random walks in random environments (RWRE) are well understood in the one-dimensional nearest-neighbor case. A surprising phenomenon is the existence of models where the walk is transient to the right, but with zero limiting velocity. More difficulties are presented by nearest-neighbor RWR
From playlist Northeastern Probability Seminar 2021
Statistics: Introduction to the Shape of a Distribution of a Variable
This video introduces some of the more common shapes of distributions http://mathispower4u.com
From playlist Statistics: Describing Data
Radek Adamczak: Functional inequalities and concentration of measure III
Concentration inequalities are one of the basic tools of probability and asymptotic geo- metric analysis, underlying the proofs of limit theorems and existential results in high dimensions. Original arguments leading to concentration estimates were based on isoperimetric inequalities, whic
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Latent Dirichlet Allocation (Part 1 of 2)
Latent Dirichlet Allocation is a powerful machine learning technique used to sort documents by topic. Learn all about it in this video! This is part 1 of a 2 video series. Video 2: https://www.youtube.com/watch?v=BaM1uiCpj_E For information on my book "Grokking Machine Learning": https:/
From playlist Unsupervised Learning
Farzana Nasrin (8/29/21): Random Persistence Diagram Generator
We will discuss in this talk a method of generating random persistence diagrams (RPDG). RPDG is underpinned (i) by a parametric model based on pairwise interacting point processes for inference of persistence diagrams (PDs) and (ii) by a reversible jump Markov chain Monte Carlo (RJ-MCMC) a
From playlist Beyond TDA - Persistent functions and its applications in data sciences, 2021
Why do prime numbers make these spirals? | Dirichlet’s theorem, pi approximations, and more
A curious pattern, approximations for pi, and prime distributions. Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Special thanks to these supporters: http://3b1b.co/spiral-thanks Based on this Math
From playlist Neat proofs/perspectives
Low moments of character sums - Adam Harper
Joint IAS/Princeton University Number Theory Seminar Topic: Low moments of character sums Speaker: Adam Harper Affiliation: University of Warwick Date: April 08, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
James Maynard: Half-isolated zeros and zero-density estimates
We introduce a new zero-detecting method which is sensitive to the vertical distribution of zeros of the zeta function. This allows us to show that there are few 'half-isolated' zeros, and allows us to improve the classical zero density result to N(σ,T)≪T24(1−σ)/11+o(1) if we assume that t
From playlist Seminar Series "Harmonic Analysis from the Edge"
Dirichlet Eta Function - Integral Representation
Today, we use an integral to derive one of the integral representations for the Dirichlet eta function. This representation is very similar to the Riemann zeta function, which explains why their respective infinite series definition is quite similar (with the eta function being an alte rna
From playlist Integrals