Random Walk and Diffusion: Basics and Applications

Related lectures (30)

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Covers the theory of Brownian motion, diffusion, and random walks, with a focus on Einstein's theory for one-dimensional motion.

Explores the estimation of expectation for discrete random variables, emphasizing its importance in probability theory.

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

Covers the basics of probability distributions, including mean, variance, and properties of random variables.

Covers Markov's and Chebyshev's Inequalities for random variables and probability distributions.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers elementary distributions like Bernoulli, Binomial, Geometric, Negative Binomial, Poisson, and Multinomial.

Explores modes of convergence in probability and statistics, illustrating concepts with examples and discussing the continuity theorem.

Covers the properties of the symmetric random walk in stochastic processes.