Contents
What is posterior in machine learning?
Posterior: Conditional probability distribution representing what parameters are likely after observing the data object. Likelihood: The probability of falling under a specific category or class.
What is a significant Bayes factor?
A Bayes factor is the ratio of the likelihood of one particular hypothesis to the likelihood of another. It can be interpreted as a measure of the strength of evidence in favor of one theory among two competing theories. It tells us what the weight of the evidence is in favor of a given hypothesis.
How to write the log _ posterior function in Jags?
The next step is to write the corresponding log_posterior (i.e., unnormalized posterior) function for both models. This function takes one draw from the joint posterior and the data object as input and returns the log of the unnormalized joint posterior density.
What do you need to know about the Jags function?
The jags function is a basic user interface for running JAGS analyses via package rjags inspired by similar packages like R2WinBUGS, R2OpenBUGS, and R2jags. The user provides a model file, data, initial values (optional), and parameters to save.
How to calculate joint posterior density in MCMC?
This function takes one draw from the joint posterior and the data object as input and returns the log of the unnormalized joint posterior density. When using MCMC software such as JAGS or Stan, specifying this function is relatively simple. As a rule of thumb, one only needs to look for all places where a “ ~ ” sign appears in the model code.
How to calculate inverse gamma prior in Jags?
We will use JAGS to fit the model which parametrizes the normal distribution in terms of the precision (i.e., one over the variance). Consequently, we implement this inverse-gamma prior on τ 2 by placing a gamma prior of the form Gamma ( α, β) on the precision; we call this precision parameter invTau2 in the code.