computational-inference

You can perform Bayesian inference using Monte Carlo methods, MCMC, and importance sampling. This Skill provides R scripts and utilizes standard packages like rstan to approximate intractable distributions and update posterior estimates without manual derivations.

Yes, you can use Stan with R to compare posterior estimates. The Skill enables you to build a Bayesian model, run provided R scripts for Bayesian updates, and contrast posterior summaries derived from different computational inference strategies.

MCMC and Gibbs sampling are the best ways to approximate expectations for high-dimensional Bayesian models. These scalable sampling methods allow you to perform computational inference and analyze complex probabilistic models effectively within R.

Yes, you need prior knowledge of probability and Bayesian statistics to use ABC. The Skill requires this foundation to perform computational inference and flexible approximation when likelihoods are difficult to compute.

Use importance sampling instead of MCMC when dealing with distributions where likelihoods are difficult to compute. Both are available for statistical inference, allowing you to choose the most flexible computational method for your probabilistic model.

Computational inference requires standard R packages like rstan to execute probabilistic modeling and sampling algorithms. These packages provide the necessary framework for immediate experimentation with Monte Carlo integration and Bayesian data analysis.