Posts tagged model comparison
Model Averaging
- 16 August 2024
When confronted with more than one model we have several options. One of them is to perform model selection as exemplified by the PyMC examples Model comparison and the GLM: Model Selection, usually is a good idea to also include posterior predictive checks in order to decide which model to keep. Discarding all models except one is equivalent to affirm that, among the evaluated models, one is correct (under some criteria) with probability 1 and the rest are incorrect. In most cases this will be an overstatment that ignores the uncertainty we have in our models. This is somewhat similar to computing the full posterior and then just keeping a point-estimate like the posterior mean; we may become overconfident of what we really know. You can also browse the blog/tag/model-comparison tag to find related posts.
Frailty and Survival Regression Models
- 16 November 2023
This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.
Bayes Factors and Marginal Likelihood
- 10 January 2023
The “Bayesian way” to compare models is to compute the marginal likelihood of each model \(p(y \mid M_k)\), i.e. the probability of the observed data \(y\) given the \(M_k\) model. This quantity, the marginal likelihood, is just the normalizing constant of Bayes’ theorem. We can see this if we write Bayes’ theorem and make explicit the fact that all inferences are model-dependant.
GLM: Model Selection
- 08 January 2022
A fairly minimal reproducible example of Model Selection using WAIC, and LOO as currently implemented in PyMC.
Bayesian Estimation Supersedes the T-Test
- 07 January 2022
Non-consecutive header level increase; H1 to H3 [myst.header]