2005/01/01 by Wolfgang Hörmann, Hörmann, Wolfgang
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Statistical Methods and Inference
paper · doi:10.57938/796ff644-690b-4fda-ba3b-8c20922293b3
openalex publication_date 2005/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
To compute the expectation of a function with respect to a multivariate distribution naive Monte Carlo is often not feasible. In such cases importance sampling leads to better estimates than the rejection method. A new importance sampling distribution, the product of one-dimensional table mountain distributions with exponential tails, turns out to be flexible and useful for Bayesian integration problems. To obtain a heavy-tailed importance sampling distribution a new radius transform for the above distribution is suggested. Together with a linear transform the new importance sampling distributions lead to simple and fast integration algorithms with reliable error bounds. (author's abstract)