Computational Statistics

Basic information


45 hours


Mathematical Statistics 


Stochastic simulation: Generation of random variables; Acceptance and rejection methods. Numerical optimization: EM algorithm; Simulated annealing. Approximate inference methods: Laplace approximation; Importance sampling; Monte Carlo Integration, Sequential Monte Carlo Methods. Monte Carlo method via Markov Chains: Gibbs sampler; Metropolis and Metropolis Hastings algorithm; Convergence diagnostics. Calculation of marginal distribution: reversible jump MCMC; Comparison of models.




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·    Del Moral, P., & Penev, S. (2017). Stochastic Processes: From Applications to Theory. CRC Press.


·       Givens, G. H., & Hoeting, J. A. (2012). Computational statistics (Vol. 710). John Wiley & Sons.
·       Wang, X., Ryan, Y. Y., & Faraway, J. J. (2018). Bayesian Regression Modeling with INLA. CRC Press.
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·       Liu, J. S. (2008). Monte Carlo strategies in scientific computing. Springer Science & Business Media.
·       Efron, Bradley, and Trevor Hastie. Computer age statistical inference. Vol. 5. Cambridge University Press, 2016.