Random Processes and Monte Carlo Simulation

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Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Covers the central limit theorem, showing how random processes converge to a normal distribution.

Covers free fields and probabilistic functions, focusing on random variables and their properties.

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Discusses conditional entropy and its role in information theory and data compression.

Explores the early days of computer simulation, focusing on the Monte Carlo method and its evolution in scientific research.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.