Posterior predictive checks (PPCs) are a great way to validate a model. The idea is to generate data from the model using parameters from draws from the ...

The most common use of sample_posterior_predictive is to perform posterior predictive checks (in-sample predictions) and new model predictions (out-of-sample ...

14 мар. 2023 г. · Posterior predictive checks can be used to mean both “comparison of posterior predictive (and pushforward quantities) to observed data” and the ...

12 июн. 2023 г. · There are two common uses of posterior predictive sampling, which we illustrate here: Performing posterior predictive checks; Obtaining out ...

If False, assumes samples are generated based on the fitting data to be used for posterior predictive checks, and samples are stored in the posterior_predictive ...

18 июл. 2023 г. · When trying to evaluate your model/posterior in the diagnostic mode, the term “posterior predictive checking” makes sense (it's a form of model ...

Posterior predictive checks (PPCs) analyze the degree to which data generated from the model deviate from data generated from the true distribution.

13 мар. 2023 г. · I am trying to understand how to use a posterior predictive check ( PPC ) after building a bayesian model using the PyMC library.

30 июн. 2023 г. · I have a specific question about the interpretation of the posterior predictive checks (PPC). First thing first, my model is a simple GP model.

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Опубликовано: 1 нояб. 2020 г.