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 ...

Sankhyā: The Indian Journal of Statistics, Series B (1960-2002), Vol. 33, No. 3/4 (Dec., 1971), pp. 217-224 (8 pages) Suppose that ${\rm L}(\psi,\theta)=(\psi -\theta)^{2}/\theta (1-\theta)$ is an ...

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 ...

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 ...