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Lecture Notes for Introductory Probability
The contents include: Combinatorics, Axioms of Probability, Conditional Probability and Independence, Discrete Random Variables, Continuous Random Variables, Joint Distributions and Independence, More on Expectation and Limit Theorems, Convergence in probability, Moment generating functions, Computing probabilities and expectations by conditioning, Markov Chains: Introduction, Markov Chains: Classification of States, Branching processes, Markov Chains: Limiting Probabilities, Markov Chains: Reversibility, Three Application, Poisson Process.
Author(s): Janko Gravner, Mathematics Department, University of California
218 Pages 
Probability Theory and Examples
This note covers measure theory, Laws of large numbers, Central limit theorem, Martingales, Markov chains, Ergodic theorems, Brownian motion, Applications to random walk, Multidimensional Brownian motion.
Author(s): Rick Durrett
490 Pages
This note explains the following topics: events and probabilities, Combining events, Conditional probabilities, independence and bayes rule, Random variables and discrete distributions, Expectation and variance, Continuous random variables.
Author(s): Sharon Goldwater, University of Edinburgh
34 Pages
Probability Theory Lecture Notes by Phanuel Mariano
The contents include: Combinatorics, Axioms of Probability, Independence, Conditional Probability and Independence, Random Variables, Some Discrete Distributions, Continuous Random Variable, Normal Distributions, Normal approximations to the binomial, Some continuous distributions, Multivariate distributions, Expectations, Moment generating functions, Limit Laws.
Author(s): Phanuel Mariano
98 Pages
Lecture Notes for Introductory Probability
The contents include: Combinatorics, Axioms of Probability, Conditional Probability and Independence, Discrete Random Variables, Continuous Random Variables, Joint Distributions and Independence, More on Expectation and Limit Theorems, Convergence in probability, Moment generating functions, Computing probabilities and expectations by conditioning, Markov Chains: Introduction, Markov Chains: Classification of States, Branching processes, Markov Chains: Limiting Probabilities, Markov Chains: Reversibility, Three Application, Poisson Process.
Author(s): Janko Gravner, Mathematics Department, University of California
218 Pages
Probability Theory and Statistics Lecture notes
The aim of the notes is to combine the mathematical and theoretical underpinning of statistics and statistical data analysis with computational methodology and practical applications. Topics covered includes: Notion of probabilities, Probability Theory, Statistical models and inference, Mean and Variance, Sets, Combinatorics, Limits and infinite sums, Integration.
Author(s): Niels Richard Hansen
294 Pages
Introduction to Probability Theory and Statistics
This note covers the following topics: Probability, Random variables, Random Vectors, Expected Values, The precision of the arithmetic mean, Introduction to Statistical Hypothesis Testing, Introduction to Classic Statistical Tests, Intro to Experimental Design, Experiments with 2 groups, Factorial Experiments, Confidence Intervals.
Author(s): Javier R. Movellan
127 Pages
Notes on Probability Theory and Statistics
This note explains the following topics: Probability Theory, Random Variables, Distribution Functions, And Densities, Expectations And Moments Of Random Variables, Parametric Univariate Distributions, Sampling Theory, Point And Interval Estimation, Hypothesis Testing, Statistical Inference, Asymptotic Theory, Likelihood Function, Neyman or Ratio of the Likelihoods Tests.
Author(s): Antonis Demos
138 Pages
Lecture Notes Probability Theory
This book explains the following topics: Probability spaces, Random variables, Independence, Expectation, Convergence of sequences of random variables.
Author(s): Manuel Cabral Morais
275 Pages