Bayesian statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Health Data Science, MSc in Quantitative Methods for ...

Bayesian inference is a statistical method of inductive reasoning based on the reassessment of competing hypotheses in the presence of new evidence. Conceptually similar to the scientific method ...

Dirichlet process (DP) priors are a popular choice for semiparametric Bayesian random effect models. The fact that the DP prior implies a non-zero mean for the random effect distribution creates an ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

This article deals with the Bayesian inference of unknown parameters of the progressively censored Weibull distribution. It is well known that for a Weibull distribution, while computing the Bayes ...

This course is available on the BSc in Actuarial Science, BSc in Business Mathematics and Statistics, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is ...

An ultimate dream in materials science is to computationally discover novel materials with desiarable properties. Recent first-principles simulations can predict properties accurately, but cannot ...