Probability and statistics for data science: math + R + data
Book information
Description
Basic probability models -- Monte Carlo simulation -- Discrete random variables: expected value -- Discrete random variables: variance -- Discrete parametric distribution families -- Continuous probability models -- Statistics: prologue -- Fitting continuous models -- The family of normal distributions -- Introduction to statistical inference -- Multivariate distributions -- The multivariate normal family of distributions -- Mixture distributions -- Multivariate description and dimension reduction -- Predictive modeling -- Model parsimony and overfitting -- Introduction to discrete time Markov chains -- Appendices: A. R Quick Start -- B. Matrix algebra.
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