Bayesian Statistical Analysis & Posterior Inference

I will conduct a Bayesian statistical analysis for your research question — fitting a Bayesian model using Stan (via brms) or PyMC, specifying priors with justification, running MCMC with convergence diagnostics, interpreting posterior distributions, calculating credible intervals and probability of direction, conducting Bayesian model comparison (LOO-CV), and delivering a results report presenting Bayesian findings clearly. Bayesian analysis is increasingly required by top psychology, medicine, and social science journals; its interpretational advantages — direct probability statements about parameters, posterior predictive checks, and principled handling of prior knowledge — are only realised when the analysis is conducted correctly.

The analysis covers prior specification with sensitivity analysis, MCMC sampling with R-hat and ESS diagnostics, trace plots, posterior predictive check, coefficient posteriors with 95% credible intervals, ROPE analysis, probability of direction, LOO-CV model comparison, and a results write-up explaining Bayesian conclusions clearly to readers familiar with frequentist reporting.

Designed for researchers required or preferring to use Bayesian inference for the interpretational richness it provides relative to frequentist hypothesis testing.