Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

A study is made of the simple empirical Bayes estimators proposed by Robbins (1956). These estimators are compared with `best' conventional estimators in terms of ...

Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 180, No. 4 (OCTOBER 2017), pp. 1191-1209 (19 pages) Area level models, such as the Fay–Herriot model, aim to improve ...

Decisions on what kind of data to collect to train a machine learning model, and how much, directly impact the accuracy and cost of that system. Bayes error *1 ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

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 Operations Research & ...