CORDIS Project
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This project develops a statistical toolbox for approximating probability distributions commonly used in data analysis. It focuses on enhancing computational efficiency through a method called Unbiased Markov Chain Monte Carlo, which is designed to leverage modern parallel computing capabilities.
This project develops a statistical toolbox for the approximation of probability distributions that commonly arise in data analysis.
The problem of approximating probabilities arise in many tasks of data science: in Bayesian statistics and its many variants, in classical hypothesis testing with p-values, in likelihood-based methods when the model involves latent variables, in models with intractable likelihoods, in the construction of knockoffs for principled variable selection, for example.
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