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Use the Metropolis-Hastings algorithm to obtain a MCMC sample from the univariate distribution: p(z) x e-/2, *E(-00,00). Use as proposal distribution: g(z\μ) 2 Nu, o), with parameter a > 0. g(z) Uniform(μ-a,u+ a), with parameter a>0. N . . If needed, calibrate the proposal distribution parameters in order to obtain visual convergence, good mixing and good auto-correlation. Discard the burn-in phase according by visual convergence observation. Record your solution samples as sample MCMC partA and sample_MCMC_partB. Include justification and any necessary graphics.

Use the Metropolis-Hastings algorithm to obtain a MCMC sample from the univariate distribution: p(z) x e-/2, *E(-00,00). Use as proposal distribution: g(z\μ) 2 Nu, o), with parameter a > 0. g(z) Uniform(μ-a,u+ a), with parameter a>0. N . . If needed, calibrate the proposal distribution parameters in order to obtain visual convergence, good mixing and good auto-correlation. Discard the burn-in phase according by visual convergence observation. Record your solution samples as sample MCMC partA and sample_MCMC_partB. Include justification and any necessary graphics.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Use the Metropolis-Hastings algorithm to obtain a MCMC sample from the univariate distribution: p(z) x el/2, E(-00, 00). Use as proposal distribution: 9(z|4) g(z|4) - N(4, 02), with parameter o >0. Uniform(u - a,u +a), with parameter a > 0. If needed, calibrate the proposal distribution parameters in order to obtain visual convergence, good mixing and good auto-correlation. Discard the burn-in phase according by visual convergence observation. Record your solution samples as sample_MCMC_partA and sample_MCMC_partB. Include justification and any necessary graphics.
Transcribed Image Text:Use the Metropolis-Hastings algorithm to obtain a MCMC sample from the univariate distribution: p(z) x el/2, E(-00, 00). Use as proposal distribution: 9(z|4) g(z|4) - N(4, 02), with parameter o >0. Uniform(u - a,u +a), with parameter a > 0. If needed, calibrate the proposal distribution parameters in order to obtain visual convergence, good mixing and good auto-correlation. Discard the burn-in phase according by visual convergence observation. Record your solution samples as sample_MCMC_partA and sample_MCMC_partB. Include justification and any necessary graphics.
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