Advanced Machine Learning and Bayesian Inference — Expert Level Reference · Introduction

Introduction

What this guide covers

Introduction Introduction Rigorous Treatment · Mathematical Inference

Overview

Topics covered

Introduction
Advanced Machine Learning and Bayesian Inference
Epistemology and Foundations of Bayesian Inference
Bayes' Theorem in Continuous Spaces
Families of Conjugated Priors
Study Case 1: Beta-Binomial Update

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

p(\theta \mid \mathcal{D}) = \frac{p(\mathcal{D} \mid \theta) \, p(\theta)}{p(\mathcal{D})} = \frac{p(\mathcal{D} \mid \theta) \, p(\theta)}{\int_{\Theta} p(\mathcal{D} \mid \theta') \, p(\theta') \, d\theta'}

View LaTeX source

p(\theta \mid \mathcal{D}) = \frac{p(\mathcal{D} \mid \theta) \, p(\theta)}{p(\mathcal{D})} = \frac{p(\mathcal{D} \mid \theta) \, p(\theta)}{\int_{\Theta} p(\mathcal{D} \mid \theta') \, p(\theta') \, d\theta'}