Statistical-Computation

This repository contains my solutions to some of the problems in the Statistical Computation course

Problem Set 2

Problem_Set_2

Topics: Poisson regression model, Newton’s Method update for the MLE estimator, Newton’s Method with the log link, Weibull distribution, MLEs using the fixed point iteration, Frechet distirbution, “rank-one update” quasi-Newton Method, “BFGS” method

Problem Set 3

Problem_Set_3

Topics: Binary-Social-Network-Classification, Local Search and Simulated Annealing, Lognormal density, Simpson’s Rule, Laplace approximation, Monte Carlo, Box-Muller method

Problem Set 4

Problem_Set_4

Topics: Variance Reduction, Control variate approach, Antithetic variate approach, Monte Carlo simulation, Empirical estimate of the percent reduction in variance, Importance Sampling, Accept-reject algorithm, Majorize, Inverse CDF method

Problem Set 5

Problem_Set_5

Topics: EM update algorithm to fit a k-Component Normal Mixture Model, Bayesian approach to fitting Normal Mixture Model, Posterior Sampling, Metropolis Sampling within Gibbs step, Metropolis-Hastings Sampling within Gibbs step, BIC

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Statistical-Computation

Problem Set 2

Topics: Poisson regression model, Newton’s Method update for the MLE estimator, Newton’s Method with the log link, Weibull distribution, MLEs using the fixed point iteration, Frechet distirbution, “rank-one update” quasi-Newton Method, “BFGS” method

Problem Set 3

Topics: Binary-Social-Network-Classification, Local Search and Simulated Annealing, Lognormal density, Simpson’s Rule, Laplace approximation, Monte Carlo, Box-Muller method

Problem Set 4

Topics: Variance Reduction, Control variate approach, Antithetic variate approach, Monte Carlo simulation, Empirical estimate of the percent reduction in variance, Importance Sampling, Accept-reject algorithm, Majorize, Inverse CDF method

Problem Set 5

Topics: EM update algorithm to fit a k-Component Normal Mixture Model, Bayesian approach to fitting Normal Mixture Model, Posterior Sampling, Metropolis Sampling within Gibbs step, Metropolis-Hastings Sampling within Gibbs step, BIC

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Name		Name	Last commit message	Last commit date
Latest commit History 27 Commits
Problem_Set_2		Problem_Set_2
Problem_Set_3		Problem_Set_3
Problem_Set_4		Problem_Set_4
Problem_Set_5		Problem_Set_5
README.md		README.md

hyaozhen/Statistical-Computation

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Statistical-Computation

Problem Set 2

Topics: Poisson regression model, Newton’s Method update for the MLE estimator, Newton’s Method with the log link, Weibull distribution, MLEs using the fixed point iteration, Frechet distirbution, “rank-one update” quasi-Newton Method, “BFGS” method

Problem Set 3

Topics: Binary-Social-Network-Classification, Local Search and Simulated Annealing, Lognormal density, Simpson’s Rule, Laplace approximation, Monte Carlo, Box-Muller method

Problem Set 4

Topics: Variance Reduction, Control variate approach, Antithetic variate approach, Monte Carlo simulation, Empirical estimate of the percent reduction in variance, Importance Sampling, Accept-reject algorithm, Majorize, Inverse CDF method

Problem Set 5

Topics: EM update algorithm to fit a k-Component Normal Mixture Model, Bayesian approach to fitting Normal Mixture Model, Posterior Sampling, Metropolis Sampling within Gibbs step, Metropolis-Hastings Sampling within Gibbs step, BIC

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