Probability and Random Processes
Explore random variables, probability distributions, and stochastic processes through intuitive explanations and practical applications in parameter estimation and noisy measurements.
Engineering Probability Lectures, Fall 2018
Comprehensive lecture series on engineering probability, covering fundamental concepts, discrete and continuous random variables, estimation techniques, and hypothesis testing.
Probability Measure
Explore set theory, fields, sigma fields, measurable spaces, and probability measures. Delve into conditional probability, independence, random variables, and distribution functions.
Multiple Random Variables - Joint and Conditional Distributions
Explore joint and conditional distributions of multiple random variables, covering discrete and continuous cases, CDFs, independence, covariance, and variance of sums.
Continuous Random Variables
Explore continuous random variables, probability density functions, and key distributions in probability theory, enhancing your understanding of statistical concepts and applications.
Discrete Random Variables
Explore discrete random variables, including binomial, hypergeometric, and Poisson distributions. Learn probability mass functions, expected values, and variance in this comprehensive introduction.
Random Variables
Explore continuous random variables, probability density functions, and the normal distribution. Learn to calculate expected values, variances, and use statistical tables for data analysis.
Edexcel S2
Comprehensive video series covering Edexcel A-Level S2 topics, including probability distributions, random variables, and hypothesis testing for exam preparation.
The Normal Distribution - Probability and Statistics
Explore the normal distribution, its importance in statistics, and the Central Limit Theorem. Learn about probability density, standard and general normal distributions, and cumulative distribution functions.
Cumulative Distribution Function - Numericals and Problems for BTech and BSc
Explore cumulative distribution functions (CDF) through practical examples, enhancing understanding of probability theory for engineering and science students.
Discrete Random Variables: Probability Density Functions and Dice Examples - Lecture 6
Master discrete random variables, probability density functions (PDFs), and cumulative density functions (CDFs) through practical examples using dice calculations and statistical analysis.
Discrete Random Variables: Bernoulli, Binomial, and Geometric - Lecture 6B
Master discrete probability concepts through Bernoulli, Binomial, and Geometric random variables, including factorial calculations and cumulative distribution functions.
Continuous Random Variables and Probability Distributions - Lecture 7A
Master continuous random variables, probability density functions, and cumulative distribution functions while exploring uniform distribution concepts and practical applications.