The use of Bayesian statistics in meat quality analyses: a review.

A Bayesian approach to meat quality analyses is discussed and compared with the classical statistical approach. Inferences from means, medians and modes of marginal posterior distributions are presented, and a variety of probability inferences and confidence intervals are presented and discussed. Classical and Bayesian theories of hypothesis testing are compared and their advantages and disadvantages are discussed. Fundamentals of Bayesian inference and Monte-Carlo Markov Chain (MCMC) techniques are presented and discussed. The great flexibility for inferences introduced by MCMC techniques is stressed. Practical examples of meat quality analyses are given, with references to available free software to analyze a large variety of models.