Statistics IS-FME-00143

Description:
Lectures:Probability: (uncertain world, perfect knowledge of the uncertainty). Counting Random variables, distributions, quantiles, mean varianceConditional probability, Bayes' theorem, base rate fallacy. Joint distributions, covariance, correlation, independence. Central limit theoremStatistics I: pure applied probability (data in an uncertain world, perfect knowledge of the uncertainty) Bayesian inference with known priors, probability intervals Conjugate priors. Applied probability (data in an uncertain world, imperfect knowledge of the uncertainty). Bayesian inference with unknown priors. Frequentist significance tests and confidence intervals,. Resampling methods: bootstrapping. Linear regression.Laboratories: Analysis of statistical algorithms. Solving problems with real data.

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