computational-inference

What problem does it solve?

This Skill enables researchers and data scientists to implement and analyze computational inference methods, including Monte Carlo integration, MCMC, importance sampling, and ABC, to solve complex probabilistic models without manual derivations.

Core Features & Use Cases

• Monte Carlo Methods: approximate expectations and integrals for intractable distributions.
• MCMC & Gibbs: scalable sampling for high-dimensional Bayesian models.
• Importance sampling & ABC: flexible inference when likelihoods are difficult to compute.
• Use Case: Build a Bayesian model for a small dataset and compare posterior estimates using different inference strategies.

Quick Start

To get started, load the included example dataset and run the provided R scripts to perform a basic Bayesian update and compare posterior summaries.