Eikosstatistics & data science

Statistical Modeling & Regression · Bayesian Statistics

Advanced Bayesian inference: Theory, computational methods, and nonparametric modeling

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Advanced Statistical Analysis · Probability Theory

Overview

Topics covered

  • Introduction
  • Bayesian Statistics: Advanced Topics
  • Foundational perspective and representation theorems
  • Foundations in Bayesian Decision Theory
  • Advanced construction of a priori distributions
  • Jeffreys Priori and Geometric Invariance

Related concepts

Main connections

  • Bayes' theorem & priors
  • Maximum likelihood estimation
  • Calculus & differentiation
  • Variance & standard deviation
  • Markov chain Monte Carlo (MCMC)
  • Normal (Gaussian) distribution

Compatible notation

Mathematical sample in LaTeX

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

ℙ(X1≤x1,X2≤x2,…,Xk≤xk)=ℙ(Xπ(1)≤x1,Xπ(2)≤x2,…,Xπ(k)≤xk),
View LaTeX source\P(X_1 \le x_1, X_2 \le x_2, \dots, X_k \le x_k) = \P(X_{\pi(1)} \le x_1, X_{\pi(2)} \le x_2, \dots, X_{\pi(k)} \le x_k),