Bayesian statistics: Multiparameter models — Advanced inference treatise · Introduction

Introduction

What this guide covers

Introduction Introduction Advanced Bayesian Statistics · Treatment of Multidimensional Spaces

Overview

Topics covered

Introduction
Multiparametric Models
Multiparametric Inference and Bayesian Philosophy
The treatment of disturbance parameters
The Complete Univariate Normal Model with Unknown Mean and Variance
Analysis under a non-informative prior distribution

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

View LaTeX source

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