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Stochastic Processes
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Learning Material
Title
Volume
Introduction to Probability: Basic concepts of probability theory, random variables, and probability distributions.
5.89 MB
<p>Introduction to Probability: Basic concepts of probability theory, random variables, and probability distributions.</p>
Markov Chains: Introduction to discrete-time Markov chains, transition probabilities, and the Chapman-Kolmogorov equation.
5.89 MB
<p>Markov Chains: Introduction to discrete-time Markov chains, transition probabilities, and the Chapman-Kolmogorov equation.</p>
Poisson Processes: Understanding Poisson processes, arrival rate, inter-arrival times, and applications.
5.89 MB
<p>Poisson Processes: Understanding Poisson processes, arrival rate, inter-arrival times, and applications.</p>
Continuous-Time Markov Chains: Extending the concept of Markov chains to continuous-time processes, including the Poisson process as a special case.
5.89 MB
<p>Continuous-Time Markov Chains: Extending the concept of Markov chains to continuous-time processes, including the Poisson process as a special case.</p>
Renewal Theory: Study of renewal processes, renewal functions, and related concepts like the renewal theorem.
5.89 MB
<p>Renewal Theory: Study of renewal processes, renewal functions, and related concepts like the renewal theorem.</p>
Brownian Motion: Introduction to continuous-time stochastic processes, properties of Brownian motion, and applications in finance and science.
5.89 MB
<p>Brownian Motion: Introduction to continuous-time stochastic processes, properties of Brownian motion, and applications in finance and science.</p>
Martingales: Understanding martingales, conditional expectations, and the optional stopping theorem.
5.89 MB
<p>Martingales: Understanding martingales, conditional expectations, and the optional stopping theorem.</p>
Limit Theorems: Convergence concepts, Central Limit Theorem, and other limit theorems in the context of stochastic processes.
5.89 MB
<p>Limit Theorems: Convergence concepts, Central Limit Theorem, and other limit theorems in the context of stochastic processes.</p>
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