Res.6-012 Introduction to Probability
Res.6-012 Introduction to Probability (Spring 2018, MIT OCW). Instructors: Prof. John Tsitsiklis and Prof. Patrick Jaillet. The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data. These tools underlie important advances in many fields, from the basic sciences to engineering and management. This resource is a companion site to 6.041SC Probabilistic Systems Analysis and Applied Probability .
(from ocw.mit.edu )

Lecture 15.1 - Overview
VIDEO

Lecture 15.1 - Overview
Lecture 15.2 - Recognizing Normal PDFs
Lecture 15.3 - Estimating a Normal Random Variable in the Presence of Additive Noise
Lecture 15.4 - The Case of Multiple Observations
Lecture 15.5 - The Mean Squared Error
Lecture 15.6 - Multiple Parameters; Trajectory Estimation
Lecture 15.7 - Linear Normal Models
Lecture 15.8 - Trajectory Estimation Illustration

Go to the Course Home or watch other lectures:

Part I: The Fundamentals
Lecture 01 - Probability Models and Axioms
Lecture s01 - Supplement: Mathematical Background
Lecture 02 - Conditioning and Bayes' Rule
Lecture 03 - Independence
Lecture 04 - Counting
Lecture 05 - Discrete Random Variables, Part I
Lecture 06 - Discrete Random Variables, Part II
Lecture 07 - Discrete Random Variables, Part III
Lecture 08 - Continuous Random Variables, Part I
Lecture 09 - Continuous Random Variables, Part II
Lecture 10 - Continuous Random Variables, Part III
Lecture 11 - Derived Distributions
Lecture 12 - Sum of Independent Random Variables Covariance and Correlation
Lecture 13 - Conditional Expectation and Variance Revisited
Part II: Inference and Limit Theorems
Lecture 14 - Introduction to Bayesian Inference
Lecture 15 - Linear Modes and Normal Noise
Lecture 16 - Least Mean Squares (LMS) Estimation
Lecture 17 - Linear Least Mean Squares (LLMS) Estimation
Lecture 18 - Inequalities, Convergence, and the Weak Law of Large Numbers
Lecture 19 - The Central Limit Theorem (CLT)
Lecture 20 - An Introduction to Classical Statistics
Part III: Random Processes
Lecture 21 - The Bernoulli Process
Lecture 22 - The Poisson Process, Part I
Lecture 23 - The Poisson Process, Part II
Lecture 24 - Finite-State Markov Chains
Lecture 25 - Steady-State Behavior of Markov Chains
Lecture 26 - Absorption Probabilities and Expected Time to Absorption