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Probability and Stochastic Processes with Applications
This text assumes no prerequisites in probability, a basic exposure to calculus and linear algebra is necessary. Some real analysis as well as some background in topology and functional analysis can be helpful. This note covers the following topics: Limit theorems, Probability spaces, random variables, independence, Markov operators, Discrete Stochastic Processes, Continuous Stochastic Processes, Random Jacobi matrices, Symmetric Diophantine Equations and Vlasov dynamics.
Author(s): Oliver Knill
382 Pages 
Probability Theory 1 Lecture Notes
The contents include: Introduction, Preliminary Results, Distributions, Random Variables, Expectation, Independence, Weak Law of Large Numbers, Borel-Cantelli Lemmas, Strong Law of Large Numbers, Random Series, Weak Convergence, Characteristic Functions, Central Limit Theorems, Poisson Convergence, Stein's Method, Random Walk Preliminaries, Stopping Times, Recurrence, Path Properties, Law of The Iterated Logarithm.
Author(s): John Pike
123 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
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
These notes are intended to give a solid introduction to Probability Theory with a reasonable level of mathematical rigor. Topics covered includes: Elementary probability, Discrete-time finite state Markov chains, Existence of Markov Chains, Discrete-time Markov chains with countable state space, Probability triples, Limit Theorems for stochastic sequences, Moment Generating Function, The Central Limit Theorem, Measure Theory and Applications.
Author(s): Christopher King
124 Pages
This note covers the following topics: Conditional expectation , Martingales , Stochastic integration-informally , Wiener process and Ito’s Formula.
Author(s): Ivan F Wilde
103 Pages
This note covers the following topics related to Probability: Kolmogorov’s axiomatization, Frequentism, Classical interpretation, Logical probability and Subjectivism.
Author(s): Branden Fitelson, Alan Hajek, and Ned Hall
30 Pages
Lecture Notes on Probability Theory and Random Processes
The goal to to help the student figure out the meaning of various concepts in Probability Theory and to illustrate them with examples. Topics covered includes: Modelling Uncertainty, Probability Space, Conditional Probability and Independence, Random Variable, Conditional Expectation, Gaussian Random Variables, Limits of Random Variables, Filtering Noise and Markov Chains
Author(s): Jean Walrand
302 Pages
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Author(s): NA
NA Pages
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Lectures on Stochastic Analysis
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Author(s): NA
NA Pages
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NA Pages
Introduction to Statistical Signal Processing Gray R.M. and Davisson L.D
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Author(s): NA
NA Pages
Probability Lecture Notes (Santos D pdf)
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