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Efficient Hamiltonian Monte Carlo for large data sets by data subsampling
[thesis]
2019
Bayesian statistics carries out inference about the unknown parameters in a statistical model using their posterior distribution, which in many cases is computationally intractable. Therefore, simulation methods such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) are frequently used to approximate the posterior distribution. SMC has the attractive ability to accurately estimate the marginal likelihood, although it is computationally more expensive than MCMC. Nevertheless,
doi:10.26190/unsworks/21695
fatcat:yortxhlzcndr7gqeidzgj7ixfi