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Mattias Villani
Professor of Statistics at Stockholm University
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Bayesian Learning
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Fork of Bayesian inference for Gaussian iid data with known variance
Bayesian Learning
Bernoulli distributionMaximum likelihood - Bernoulli dataBayes' theorem for eventsBayesian inference for Bernoulli iid dataBayesian inference for Gaussian iid data with known varianceMaximum likelihood for iid Poisson dataBayesian inference for iid Poisson countsBayesian inference for Exponential iid dataBayesian credible intervalsScaled inverse chi2 distribution
Bayesian inference for Gaussian iid data
Bayesian inference for multinomial dataAutoregressive processes - simulation and priorsPrior predictive - iid Poisson model with Gamma priorMultivariate normal distributionBayesian linear regressionCentral limit theoremLaw of Large NumbersThe Taylor approximationSecond derivative measures the curvature of a functionRandom Walk MetropolisHamiltonian Markov Chain Monte CarloLeapfrog IntegratorHMC sampling from multi-modal distributionsPosterior approximation - Beta model for proportionsDirichlet DistributionDistributions on the unit simplexMaximum likelihood - Poisson regressionKullback-Leibler divergence - continuous distributionsConfidence interval for a proportionCoverage of interval estimates for a proportionApproximating the Binomial distributionSampling distribution and likelihood function - Normal modelSampling distribution and likelihood function - Bernoulli modelBayesian hypothesis test for a mean in a normal populationDistribution of the maximum - a tale of tails
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Platform
  • Observable Canvases
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  • Pricing
Docs
  • Observable
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  • D3
  • Release notes
Resources
  • Blog
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  • Forum
Company
  • About
  • Careers
  • GitHub
GitHubLinkedInBlueskyYouTubeX
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