Virtually all computations performed by the nervous system are subject to uncertainty and taking this into account is critical for making inferences about the outside world. For instance, imagine ...

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 ...

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 62, No. 1 (2000), pp. 57-75 (19 pages) Hidden Markov models form an extension of mixture models which provides a ...

In our statistical practice, we ecologists work comfortably within the hypothetico-deductive epistemology of Popper and the frequentist statistical methodology of Fisher. Consequently, our null ...

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 ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

This course is available on the BSc in Actuarial Science, BSc in Actuarial Science (with a Placement Year), BSc in Data Science, BSc in Mathematics with Data Science, BSc in Mathematics with Economics ...

The increasing interest in Bayesian group sequential design is due to its potential to reinforce efficiency in clinical trials, shorten drug development time, and enhance the accuracy of statistical ...

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 ...