Stochastic Processes: Definitions and Examples

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Conditional Probability: Prediction Decomposition

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Dependence and Correlation

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Linear Combinations: Moment-Generating Functions

Explores moment-generating functions, linear combinations, and normality of random variables.

Transformations of Joint Densities

Covers the transformations of joint continuous densities and their implications on probability distributions.

Probability and Statistics: Fundamentals

Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.

Statistics for Data Science: Basics and Modelling

Introduces statistical modelling basics, probability theory, and key concepts for data science applications.

Probability and Statistics

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Introduction to Inference

Covers the basics of probability theory, random variables, joint probability, and inference.

Probability and Statistics

Covers probability, statistics, independence, covariance, correlation, and random variables.

Conditional Density and Expectation

Covers conditional density, independence of random variables, expectation, and variance calculation.