Professor of Statistics at Stockholm University
Bernoulli distributionMaximum likelihood - Bernoulli dataBayes' theorem for eventsBayesian inference for Bernoulli iid dataMaximum likelihood for iid Poisson dataBayesian inference for iid Poisson countsBayesian inference for Exponential iid dataScaled inverse chi2 distributionBayesian inference for multinomial dataBayesian inference for Gaussian iid data with known varianceBayesian credible intervalsAutoregressive processes - simulation and priorsPrior predictive - iid Poisson model with Gamma priorMultivariate normal distributionBayesian linear regressionCentral limit theoremLaw of Large NumbersRandom Walk MetropolisHamiltonian Markov Chain Monte CarloLeapfrog IntegratorHMC sampling from multi-modal distributionsPosterior approximation - Beta model for proportionsDistributions on the unit simplexMaximum likelihood - Poisson regressionSecond derivative measures the curvature of a functionBayesian inference for Gaussian iid dataKullback-Leibler divergence - continuous distributionsConfidence interval for a proportionCoverage of interval estimates for a proportionSampling 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 tailsCompound-Gamma distribution
Approximating the Binomial distribution
high = Math.ceil(jstat.normal.inv(0.9999, params[0]*params[1], Math.sqrt(params[0]*params[1]*(1-params[1]))))
low = Math.floor(jstat.normal.inv(0.0001, params[0]*params[1], Math.sqrt(params[0]*params[1]*(1-params[1]))))
legenddomain = ["binomial", "normal", "normal with correction", "poisson"]
legendcolor = ["#C1CFE4", "#AB8D61", "#007878", "#780000"]
boolselect = [true,normalapprox, normalapproxcorr, poisapprox]
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