Lectures related to Probability mass function

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Propagation of Uncertainty: Probability Density Function

Covers the propagation of uncertainty through the generation of probability density functions.

Continuous Random Variables: Basics

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Astrophysical Fluids & Plasmas

Explores astrophysical fluids, plasmas, MHD, turbulence, and plasma oscillations, including Faraday rotation for measuring cosmic magnetic fields.

Quantum Chemistry

Covers quantum chemistry topics such as tunneling, wave packets, and harmonic oscillators.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Quantum Physics I

Covers the basics of quantum physics, including wave packet dynamics and probability density functions.

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.

Variance of Random Variables

Covers the concept of variance for random variables and introduces calculation rules.

Bit Error Rate and Receiver Sensitivity

Explores bit error rate and receiver sensitivity in optical communication systems, covering BER, receiver sensitivity, probability density functions, and error probability calculations.

Discrete Random Variables: Functions and Probabilities

Explores discrete random variables, their functions, and probabilities in various scenarios.

Linear Regression: Theory and Applications

Covers the theory and practical applications of linear regression.

Probability Theory: Discrete Random Variables

Covers discrete random variables, probability mass function, properties, and binomial distribution with illustrative examples.

Signal Processing: Filter Synthesis and Random Variables

Explores filter synthesis methods and the role of random variables in measurement systems.

Exercise 2: Series 22

Covers the solution of exercise 2 from series 22.

Calculations of Expectation

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Discrete Random Variables

Covers properties and transformations of discrete random variables, focusing on PMF and expectation.

Statistics for Data Science: Basics and Modelling

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