In this lecture, the professor discussed classical inference, Linear regression, and binary hypothesis testing.

Lecture 24: Classical Inference II

In this lecture, the professor discussed Bayesian statistical inference, least means squares, and linear LMS estimation.

Lecture 22: Bayesian Statistical Inference II

In this lecture, the professor discussed classical statistics, maximum likelihood (ML) estimation, and confidence intervals.

Lecture 23: Classical Statistical Inference I

In this lecture, the professor discussed Bayesian statistical inference and inference models.

Lecture 21: Bayesian Statistical Inference I

In this lecture, the professor discussed classical inference, simple binary hypothesis testing, and composite hypotheses testing.

Lecture 25: Classical Inference III

In this lecture, the professor discussed Markov process definition, n-step transition probabilities, and classification of states.

Lecture 16: Markov Chains I

In this lecture, the professor discussed Markov process, steady-state behavior, and birth-death processes.

Lecture 17: Markov Chains II

In this lecture, the professor discussed Poisson process, merging, splitting, and random incidence.

Lecture 15: Poisson Process II

In this lecture, the professor discussed Markov Processes, probability of blocked phone calls, absorption probabilities, and calculating expected time to absorption.

Lecture 18: Markov Chains III

In this lecture, the professor discussed central limit theorem, Normal approximation, 1/2 correction for binomial approximation, and De Moivre–Laplace central limit theorem.

Lecture 20: Central Limit Theorem

Probabilistic Systems Analysis and Applied Probability

This is an excellent course. I am preparing for an exam in May 2013. This resource has proved to be a life saver. It&#39;s  comprehensive, easy to understand and rigorous. Thank you so much OCW and professor John.

Excellent

In this lecture, the professor discussed probability as a mathematical framework, probabilistic models, axioms of probability, and gave some simple examples.

Lecture 1: Probability Models and Axioms

In this lecture, the professor discussed multiple random variables: conditioning and independence.

Lecture 9: Multiple Continuous Random Variables

In this lecture, the professor discussed conditional probability, multiplication rule, total probability theorem, and Bayes' rule.

Lecture 2: Conditioning and Bayes' Rule

In this lecture, the professor discussed derived distributions, convolution, covariance and correlation.

Lecture 11: Derived Distributions (ctd

In this lecture, the professor discussed principles of counting, permutations, combinations, partitions, and binomial probabilities.

Lecture 4: Counting

In this lecture, the professor discussed probability density functions, cumulative distribution functions, and normal random variables.

Lecture 8: Continuous Random Variables

In this lecture, the professor discussed Bernoulli process, random processes, basic properties of Bernoulli process, distribution of interarrival times, the time of the kth success, merging and splitting.

Lecture 13: Bernoulli Process

In this lecture, the professor discussed multiple random variables, expectations, and binomial distribution.

Lecture 7: Discrete Random Variables III

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