Bayesian analysis is a statistical method used to update the probability of a hypothesis as new evidence or data becomes available. It allows for the incorporation of prior knowledge or beliefs into the analysis, providing more accurate and reliable estimates and inferences compared to frequentist methods. The purpose of Bayesian analysis is to quantify uncertainty, make predictions, and infer causal relationships within a probabilistic framework.

There are lots of applications of statistics in bioinformatics - it is one of the central areas of studies when learning bioinformatics. For example, students have to learn about Bayesian and Frequentist statistics as well as Hidden Markov Models (HMMs). Statistics can answer questions such as, "what is the likelihood that these two DNA sequence alignments are due to them being homologous?" or "given that this sequence looks like this, what is the probability that it is a gene?"

A prior parameter is a parameter that is specified before analyzing the data, usually based on prior knowledge or beliefs. A post hoc parameter is a parameter that is determined after analyzing the data, often through exploratory analysis or hypothesis testing. Prior parameters are often used in Bayesian statistics, while post hoc parameters are common in frequentist statistics.