MitISEM

R package MitISEM: Mixture of Student t Distributions using Importance Sampling and Expectation Maximization. Flexible multivariate function approximation using adapted Mixture of Student t Distributions. Mixture of t distribution is obtained using Importance Sampling weighted Expectation Maximization algorithm.

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References in zbMATH (referenced in 4 articles )

Showing results 1 to 4 of 4.
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  1. Dellaportas, Petros; Tsionas, Mike G.: Importance sampling from posterior distributions using copula-like approximations (2019)
  2. Geweke, John; Durham, Garland: Sequentially adaptive Bayesian learning algorithms for inference and optimization (2019)
  3. Nalan Baştürk and Stefano Grassi and Lennart Hoogerheide and Anne Opschoor and Herman van Dijk: The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference (2017) not zbMATH
  4. Hoogerheide, Lennart; Opschoor, Anne; van Dijk, Herman K.: A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation (2012)