How to Use Experimental Data to Compute the Probability of Your Theory

TL;DR

This paper guides theorists and experimentalists on using Bayesian methods with experimental data to estimate model parameters and limits, emphasizing practical implementation and understanding of the approach.

Contribution

It provides a practical introduction and Mathematica code for Bayesian parameter estimation, bridging the gap between theoretical models and experimental data.

Findings

Demonstrates how to compute probabilities of theories using experimental data

Highlights advantages and limitations of Bayesian methods

Provides accessible Mathematica code for practitioners

Abstract

This article is geared towards theorists interested in estimating parameters of their theoretical models, and computing their own limits using available experimental data and elementary Mathematica code. The examples given can be useful also to experimentalists who wish to learn how to use Bayesian methods. A thorough introduction precedes the practical part, to make clear the advantages and shortcomings of the method, and to prevent its abuse. The goal of this article is to help bridge the gap between theory and experiment.