Bayesian

1 Conditional Probability and Bayes

#Bayesian #Bayes's Theorem #Conditional Probability

Skeleton notes for conditional probability and Bayes' theorem

2 Least Squares, Bootstrap, Maximum Likelihood, and Bayesian

#Bayesian #Least Squares #Bootstrap #Maximum Likelihood #Bayesian

Relation and difference between different methods in regression

3 EM Methods

#Maximum Likelihood #Bayesian #Sampling #Gaussian Mixture

EM method, expectation-maximization algorithm, is an inspiring iterative method to find the log-likelihood by introducing some intermediate variable such as responsibility.

4 Variantional Inference Normalizing Flow

#Variational Inference #Normalizing Flow #Bayesian

Topics Variational Inference Normalizing Flow Variational Inference with Normalizing Flows

29 Hamilton WL. Graph Representation Learning. Chapter 8

#Time series #Machine Learning #Bayesian

Traditional GraphGeneration Approaches

30 Hamilton WL. Graph Representation Learning. Chapter 8 (2)

#Time series #Machine Learning #Bayesian

Traditional GraphGeneration Approaches

31 Uncertainty in Deep Learning

#Machine Learning #Bayesian #Uncertainty

Topic: uncertainty in deep learning References: Gawlikowski, J. et al. A Survey of Uncertainty in …

36 Evaluating time series forecasting models

#Time series #Machine Learning #Bayesian

Our topic for this session is Cerqueira V, Torgo L, Mozetic I. Evaluating time series forecasting …

Inferring causal impact using Bayesian structural time-series models

#Time series #Machine Learning #Bayesian

Our topic for this session is Inferring causal impact using Bayesian structural time-series models …

References for Probability Estimation Club

#Bayesian #Least Squares #Bootstrap #Maximum Likelihood #Bayesian #Normalizing Flow

A list of references for our online discussions.