Bayesian statistics in contemporary data science signify a paradigm revolution in probabilistic argumentation that provides ...

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

In Bayesian statistical inference, prior probability is the probability of an event occurring before new data is collected. In other words, it represents the best rational assessment of the ...

The first part of the module covers the basic concepts of Bayesian Inference such as prior and posterior distribution, Bayesian estimation, model choice and forecasting. These concepts are also ...

The suggested augmentation robustifies the baseline parametric model to local misspecification, while preserving the appeal of Bayesian inference. We develop an MCMC algorithm for the augmented model ...

The integration of Bayesian statistics into modern analytics has redefined industries, and Alexandre Andorra’s work exemplifies its transformative potential. With expertise spanning sports analytics, ...