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Basic probability theory
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 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
Theory of Probability by Prof. Scott Sheffield
This note covers topics such as sums of independent random variables, central limit phenomena, infinitely divisible laws, Levy processes, Brownian motion, conditioning, and martingales.
Author(s): Prof. Scott Sheffield
NA 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
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
Probability and Stochastic Processes
This book covers the following topics: Basic Concepts of Probability Theory, Random Variables, Multiple Random Variables, Vector Random Variables, Sums of Random Variables and Long-Term Averages, Random Processes, Analysis and Processing of Random Signals, Markov Chains, Introduction to Queueing Theory and Elements of a Queueing System.
Author(s): Dongyu Qiu
NA Pages