A full Bayesian approach is used as a basis of inference and prediction. Computations are performed using Markov chain Monte Carlo methods. A key benefit of this approach is the ability to obtain a ...

Generative AI Beyond the Black Box: A Bayesian Model for LLM Reasoning, Inference and Applications Abstract Generative AI, based on Large Language Models (LLMs), has become very popular. In this talk, ...

Bayesian estimation methods form a dynamic branch of statistical inference, utilising Bayes’ theorem to update probabilities in light of new evidence. This framework combines prior knowledge ...

We present a number of examples and develop techniques for parameter estimation for general severity and frequency distribution models from a Bayesian perspective. Finally, we introduce and evaluate ...

Bayesian statistics has emerged as a powerful methodology for making decisions from data in the applied sciences. Bayesian brings a new way of thinking to statistics, in how it deals with probability, ...

Composite likelihoods are increasingly used in applications where the full likelihood is analytically unknown or computationally prohibitive. Although some frequentist properties of the maximum ...

Bayesian Inference: Bayes theorem, prior, posterior and predictive distributions, conjugate models (Normal-Normal, Poisson-Gamma, Beta-Binomial), Bayesian point estimation, credible intervals and ...

Bayesian Inference: Bayes theorem, prior, posterior and predictive distributions, conjugate models (Normal-Normal, Poisson-Gamma, Beta-Binomial), Bayesian point estimation, credible intervals and ...