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Metropolis–Hastings algorithm
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11	Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent D´epartement d’informatique et recherche op´erationnelle, Universit´e de Montr´eal Abstract Add to Reading List Source URL: papers.nips.cc Language: English - Date: 2014-02-24 18:37:23 Statistical inference Monte Carlo methods Markov models Maximum likelihood Metropolis–Hastings algorithm Estimator Markov chain Multivariate normal distribution Bayesian network Statistics Estimation theory M-estimators
12	The Markov Chain Monte Carlo Revolution Persi Diaconis∗ Abstract The use of simulation for high dimensional intractable computations has revolutionized applied mathematics. Designing, improving and understanding the ne Add to Reading List Source URL: statweb.stanford.edu Language: English - Date: 2008-08-22 18:55:55 Symbol Metropolis–Hastings algorithm Normal distribution Itō diffusion Statistics Markov chain Permutation
13	DISTRIBUTED AVERAGE CONSENSUS USING PROBABILISTIC QUANTIZATION Tuncer C. Aysal, Mark Coates, and Michael Rabbat Department of Electrical and Computer Engineering, McGill University, Montreal, QC {tuncer.aysal,mark.coates Add to Reading List Source URL: www.tsp.ece.mcgill.ca Language: English - Date: 2007-07-05 11:41:38 Probability and statistics Digital audio Quantization Consensus Normal distribution Variance Metropolis–Hastings algorithm Perceptron Statistics Digital signal processing Noise
14	Loss-calibrated Monte Carlo Action Selection Ehsan Abbasnejad Justin Domke NICTA & ANU Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2014-11-21 10:16:12 Statistical inference Monte Carlo methods Bayes estimator Bayesian inference Estimator Metropolis–Hastings algorithm Importance sampling Bias of an estimator Loss function Statistics Estimation theory Statistical theory
15	Part A Simulation and Statistical Programming HT 2015 Problem Sheet 3 due Week 7 Tuesday 10am 1. (a) Give a Metropolis-Hastings algorithm to sample according to the Gamma probability density function, p(x) ∝ xα−1 ex Add to Reading List Source URL: www.stats.ox.ac.uk Language: English - Date: 2015-02-23 13:52:48 Computational statistics Probability and statistics Markov models Markov processes Metropolis–Hastings algorithm Markov chain Gibbs sampling Itō diffusion Bayesian inference in phylogeny Statistics Monte Carlo methods Markov chain Monte Carlo
16	CURRICULUM VITAE Jeffrey S. Rosenthal (Last updated April 16, BIOGRAPHICAL INFORMATION: Personal: Add to Reading List Source URL: probability.ca Language: English - Date: 2015-04-16 10:11:05 Stochastic processes Markov chain Monte Carlo Markov processes Jeffrey S. Rosenthal Markov chain Metropolis–Hastings algorithm Random walk Stochastic Gibbs sampling Statistics Markov models Monte Carlo methods
17	MONTE CARLO SIMULATIONS Current physics research 1) Theoretical 2) Experimental 3) Computational Monte Carlo (MC) Methodused to Add to Reading List Source URL: www.complex.nbi.dk Language: English - Date: 2006-08-25 11:14:02 Statistical mechanics Markov models Monte Carlo methods Markov processes Ising model Markov chain Detailed balance Metropolis–Hastings algorithm Importance sampling Physics Statistics Probability and statistics
18	Supplement to ”Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions” Scot M. Miller, Anna M. Michalak, and Patricia J. Levi This supplement describes in greater detail the mult Add to Reading List Source URL: www.geosci-model-dev.net Language: English - Date: 2014-12-04 04:35:21 Probability and statistics Gibbs sampling Metropolis–Hastings algorithm Realization Normal distribution Prior probability Multiple-try Metropolis Markov chain Monte Carlo Statistics Monte Carlo methods
19	Exponential Model Selection (in NMR) Using Bayesian Probability Theory G. LARRY BRETTHORST,1 WILLIAM C. HUTTON,1 JOEL R. GARBOW,1,2 JOSEPH J.H. ACKERMAN1–3 Add to Reading List Source URL: bayes.wustl.edu Language: English - Date: 2010-12-21 17:21:39 Probability and statistics Bayesian inference Statistical theory Bayesian probability Markov chain Bayes factor Probability distribution Estimation theory Metropolis–Hastings algorithm Statistics Bayesian statistics Statistical inference
20	Walk, Not Wait: Faster Sampling Over Online Social Networks Azade Nazi‡ , Zhuojie Zhou†† , Saravanan Thirumuruganathan‡ , Nan Zhang†† , Gautam Das‡ ‡ ‡ Add to Reading List Source URL: www.vldb.org Language: English - Date: 2015-01-10 21:47:47 Statistical inference Non-uniform random numbers Markov chain Monte Carlo Random walk Rejection sampling Metropolis–Hastings algorithm Sample size determination Estimation theory Connectivity Statistics Monte Carlo methods Sampling

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