Bayesian inference | Vose Software

Bayesian inference

Bayesian inference models the process of learning. That is, we start with a certain level of belief, however vague, and through the accumulation of experience, our belief becomes more fine-tuned. Some people take a dislike to Bayesian inference because it is overtly subjective and they like to think of statistics as being objective. We don't agree, because any statistical analysis is necessarily subjective as a result of the need to make assumptions, but also because many approximations are accepted without question, even without warning that they have been made. For that reason we appreciate the extra transparency of Bayesian inference, but it also frequently provides answers where classical statistics cannot. Perhaps more importantly for us as risk analysts, Bayesian inference encourages our clients to think about the level of knowledge they have about their problem, and what that means to them.

Bayesian inference is an extremely powerful technique, based on Bayes' Theorem (sometimes called  Bayes' Formula), for using data to improve one's estimate of a parameter. There are essentially three steps involved:

  1. Constructing a confidence distribution of the parameter before analyzing the new data set. This is called the prior distribution;

  2. Find an appropriate likelihood function for the observed data; and

  3. Modify the prior distribution using the likelihood function to get a revised estimate known as the posterior distribution.

Bayesian inference options

This section starts with some introductory topics:

Introduction to the concept and some simple examples

How to determine prior distributions

How to determine likelihood functions

We then turn to the actual execution of a Bayesian inference for risk analysis models with ModelRisk by looking at various techniques for arriving at the posterior distribution:

Construction method

Conjugate prior method

Simulation with accept/reject method

Markov Chain Monte Carlo method

Most important of all, we offer a number of worked examples:

Examples of Bayesian inference calculations

General estimation problems

Identifying a weighted coin

A simple Bayesian inference example using construction

Tigers in the jungle

Simple construction model showing the interaction between likelihood functions and informed priors

Gender of a random sample of people

A simple construction model illustrating the importance of the prior distribution

Micro-fractures on turbine blades

A model to show how to incorporate hyperparameters by simulation, as well as offering both simulation and construction approaches to determining the posterior distribution

The Monty Hall problem

A fun model of a classic problem showing the sometimes unintuitive nature of the Bayesian result

Bayesian estimation of a components mean time to failure MTTF

A simple construction example that shows how we use data that describe being above or below a threshold, instead of exact observations


Taylor series approximation to a Bayesian posterior distribution

Showing how Taylor series expansion lets you determine the normal approximation to posterior distributions, and a method for algebraically obtaining the standard deviation

Normal approximation to the Beta posterior distribution

Example of a Taylor series expansion


Two common statistical problems

Estimate of the mean of a Normal distribution with unknown standard deviation

A standard statistics problem with the same outcome as the classical method


Bayesian estimate of the mean of a Normal distribution with known standard deviation

Another standard statistics problem with the same outcome as the classical method