This repository contains a prototype implementation of the optimal sampling algorithms from: Feras A. Saad, Cameron E. Freer, Martin C. Rinard, and Vikash K. Mansinghka. Optimal Approximate Sampling ...
We have previously discussed the importance of estimating uncertainty in our measurements and incorporating it into data analysis 1. To know the extent to which we can generalize our observations, we ...
We have previously discussed the importance of estimating uncertainty in our measurements and incorporating it into data analysis 1. To know the extent to which we can generalize our observations, we ...
Sampling Distributions. Suppose that we draw all possible samples of size n from a given population. Suppose further that we compute a statistic (e.g., a mean, proportion, standard deviation) for each ...
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...
A rejection sampling scheme for posterior probability distributions via the ratio-of-uniforms method
Abstract: Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density ...
Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Thomas J. Brock is a CFA and CPA with more ...
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