Category: Monte Carlo Statistical Methods

• Phase Plane plots of COVID-19 deaths with uncertainties

  American Statistical Association . Andrew Harvey . anomaly detection . count data regression . COVID-19 . dependent data . dlm package . Durbin and Koopman . dynamic linear models . epidemiology . filtering . forecasting . Kalman filter . LaTeX . model-free forecasting . Monte Carlo Statistical Methods . numerical algorithms . numerical linear algebra . population biology . population dynamics . prediction . R . R statistical programming language . regression . statistical learning . stochastic algorithms

  Oct 30.2020
• Calculating Derivatives from Random Forests

  bridge to somewhere . Calculus . dependent data . dynamic generalized linear models . dynamical systems . ensemble methods . ensemble models . filtering . forecasting . hierarchical clustering . linear regression . model-free forecasting . Monte Carlo Statistical Methods . non-mechanistic modeling . non-parametric model . non-parametric statistics . numerical algorithms . prediction . R statistical programming language . random forests . regression . sampling . splines . statistical learning . statistical series . statistics . time derivatives . time series

  Jun 27.2020
• Reanalysis of business visits from deployments of a mobile phone app

  Bayesian computational methods . biology . capture-mark-recapture . capture-recapture . Christian Robert . count data regression . cumulants . diffusion . diffusion processes . Ecological Society of America . ecology . epidemiology . experimental science . field research . Gibbs Sampling . Internet measurement . Jean-Michel Marin . linear regression . mark-recapture . mathematics . maximum likelihood . Monte Carlo Statistical Methods . multilist methods . multivariate statistics . non-mechanistic modeling . non-parametric statistics . numerics . open source scientific software . Pierre-Simon Laplace . population biology . population dynamics . quantitative biology . quantitative ecology . R . R statistical programming language . sampling . sampling algorithms . segmented package in R . statistical ecology . statistical models . statistical regression . statistical series . statistics . stepwise approximation . stochastic algorithms . surveys . V. M. R. Muggeo

  Feb 20.2020
• Sampling: Rejection, Reservoir, and Slice

  accept-reject methods . American Statistical Association . Bayesian computational methods . catastrophe modeling . data science . diffusion processes . empirical likelihood . Gibbs Sampling . insurance . Markov Chain Monte Carlo . mathematics . Mathematics and Climate Research Network . maths . Monte Carlo Statistical Methods . multivariate statistics . numerical algorithms . numerical analysis . numerical software . numerics . percolation theory . Python 3 programming language . R statistical programming language . Radford Neal . sampling . slice sampling . spatial statistics . statistics . stochastic algorithms . stochastic search

  Sep 29.2018
• A quick note on modeling operational risk from count data

  American Statistical Association . Bayesian . Bayesian computational methods . count data regression . dichotomising continuous variables . dynamic generalized linear models . Frank Harrell . Frequentist . Generalize Additive Models . generalized linear mixed models . generalized linear models . GLMMs . GLMs . John Kruschke . maximum likelihood . model comparison . Monte Carlo Statistical Methods . multivariate statistics . nonlinear . numerical software . numerics . premature categorization . probit regression . statistical regression . statistics

  Sep 11.2018
• Repaired R code for Markov spatial simulation of hurricane tracks from historical trajectories

  American Meteorological Association . American Statistical Association . AMETSOC . Arthur Charpentier . atmosphere . diffusion . diffusion processes . dynamic linear models . dynamical systems . environment . geophysics . hurricanes . Kalman filter . Kerry Emanuel . Lévy flights . Lorenz . Markov chain random fields . mathematics . mathematics education . maths . MCMC . mesh models . meteorological models . meteorology . model-free forecasting . Monte Carlo Statistical Methods . numerical analysis . numerical software . oceanography . open data . open source scientific software . physics . random walk processes . random walks . science . spatial statistics . state-space models . statistical dependence . statistics . stochastic algorithms . stochastics . time series

  Aug 11.2016
• On Smart Data

  Akaike Information Criterion . Bayes . Bayesian . Bayesian inversion . big data . bigmemory package for R . changepoint detection . data science . data streams . dlm package . dynamic generalized linear models . dynamic linear models . dynamical systems . Generalize Additive Models . generalized linear models . information theoretic statistics . Kalman filter . linear algebra . logistic regression . machine learning . Markov Chain Monte Carlo . mathematics . mathematics education . maths . maximum likelihood . MCMC . Monte Carlo Statistical Methods . multivariate statistics . numerical analysis . numerical software . numerics . quantitative biology . quantitative ecology . rationality . reasonableness . sampling . smart data . state-space models . statistical dependence . statistics . the right to know . time series

  Jun 11.2016
• Cory Lesmeister’s treatment of Simson’s Paradox (at “Fear and Loathing in Data Science”)

  Akaike Information Criterion . approximate Bayesian computation . Bayes . Bayesian . evidence . Frequentist . games of chance . information theoretic statistics . Kalman filter . likelihood-free . mathematics . maths . maximum likelihood . Monte Carlo Statistical Methods . probabilistic programming . rationality . Rauch-Tung-Striebel . Simpson’s Paradox . state-space models . statistical dependence . statistics . stochastics

  May 7.2016
• “Lucky d20” (by Tamino, with my reblogging comments)

  Bayes . Bayesian . card decks . card draws . card games . chance . D&D . Dungeons and Dragons . games of chance . mathematics . maths . Monte Carlo Statistical Methods . probability . statistical dependence . statistics . stochastic algorithms . stochastics . Wizards of the Coast

  May 1.2016
• p-values and hypothesis tests: the Bayesian(s) rule

  approximate Bayesian computation . arXiv . Bayes . Bayesian . Bayesian inversion . bollocks . Christian Robert . climate . complex systems . data science . Frequentist . information theoretic statistics . likelihood-free . Markov Chain Monte Carlo . MCMC . Monte Carlo Statistical Methods . population biology . rationality . reasonableness . science . scientific publishing . statistical dependence . statistics . stochastics . Student t distribution

  Mar 7.2016

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  “Grid shading by simulated annealing” [Martyn Plummer]

  approximate Bayesian computation . Bayesian . Bayesian inversion . Boltzmann . BUGS . Christian Robert . Gibbs Sampling . JAGS . likelihood-free . Markov Chain Monte Carlo . Martyn Plummer . mathematics . maths . MCMC . Monte Carlo Statistical Methods . optimization . probabilistic programming . SPSA . stochastic algorithms . stochastic search

  Feb 9.2016
• high dimension Metropolis-Hastings algorithms

  Bayes . Bayesian . Bayesian inversion . boosting . chance . Christian Robert . computation . ensembles . Gibbs Sampling . James Spall . Jerome Friedman . Markov Chain Monte Carlo . mathematics . maths . MCMC . Monte Carlo Statistical Methods . multivariate statistics . numerical software . numerics . optimization . reasonableness . Robert Schapire . SPSA . state-space models . statistics . stochastic algorithms . stochastic search . stochastics . Yoav Freund

  Feb 6.2016