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 ...

Eduardo Ley, Mark F. J. Steel, On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression, Journal of Applied Econometrics, Vol. 24, No. 4 (Jun. - Jul., 2009 ...

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 ...

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, ...

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

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, ...