Bayesian statistics · Linear and generalized models · Introduction

Introduction

What this guide covers

Introduction Treatise on mathematical statistics

Overview

Topics covered

Introduction
Bayesian Modeling: Rigorous Analysis of Linear Models and GLMs
Bayesian Epistemology and Foundations of Inference
Frequentist Approach
Bayesian Approach
The Classical Bayesian Linear Model

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

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

p(\boldsymbol{\theta} \mid \mathbf{y}) = \frac{p(\mathbf{y} \mid \boldsymbol{\theta}) \, p(\boldsymbol{\theta})}{p(\mathbf{y})} = \frac{p(\mathbf{y} \mid \boldsymbol{\theta}) \, p(\boldsymbol{\theta})}{\int_{\Theta} p(\mathbf{y} \mid \boldsymbol{\theta}) \, p(\boldsymbol{\theta}) \, d\boldsymbol{\theta}}

View LaTeX source

p(\boldsymbol{\theta} \mid \mathbf{y}) = \frac{p(\mathbf{y} \mid \boldsymbol{\theta}) \, p(\boldsymbol{\theta})}{p(\mathbf{y})} = \frac{p(\mathbf{y} \mid \boldsymbol{\theta}) \, p(\boldsymbol{\theta})}{\int_{\Theta} p(\mathbf{y} \mid \boldsymbol{\theta}) \, p(\boldsymbol{\theta}) \, d\boldsymbol{\theta}}