Nature

Bayesian Predictive Density Estimation

Bayesian predictive density estimation represents a cornerstone of modern statistical inference by integrating prior knowledge with observed data to produce a predictive distribution for future ...
JSTOR Daily

Smooth Empirical Bayes Estimation for One-Parameter Discrete Distributions

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 ...
JSTOR Daily

The Use of Empirical Bayes Estimators in a Linear Regression Model

New estimators for the parameters in the linear regression model are presented, using not only the usual random sample of observations, but also past experience in the form of previous estimates of ...
Risk

Bayesian nonparametric covariance estimation with noisy and nonsynchronous asset prices

The covariance matrix of asset returns is the key input for many problems in finance and economics. This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from ...
Risk

Robust calibration of financial models using Bayesian estimators

The authors consider a general calibration problem for derivative pricing models, which they reformulate into a Bayesian framework to attain posterior distributions for model parameters. They then ...
lse

Bayesian Machine Learning

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 & ...