Course

MGT-484: Applied probability & stochastic processes

Summary

This course focuses on dynamic models of random phenomena, and in particular, the most popular classes of such models: Markov chains and Markov decision processes. We will also study applications in queuing theory, finance, project management, etc.

Lectures

Instructor

Andrés Ignacio Cristi Espinosa

Expertise

Online Algorithms & Sequential Decision-Making- Algorithmic Game Theory- Mechanism & Market Design Teaching & PhD PhD Students Jiechen Zhang, Aruzhan Amanbayeva Courses Algorithmic game theory MGT-435 We will study mathematical models of the interplay between algorithms and strategic behavior. We cover fundamental concepts from game theory and mechanism design, including Nash equilibria, the price of anarchy, auctions and market design, incentive compatibility, and online learn

Official source

https://edu.epfl.ch/coursebook/en/applied-probability-stochastic-processes-MGT-484

Moodle Page

http://go.epfl.ch/MGT-484

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Summary

Lectures

Instructor

Andrés Ignacio Cristi Espinosa

Expertise

Online Algorithms & Sequential Decision-Making- Algorithmic Game Theory- Mechanism & Market Design Teaching & PhD PhD Students Jiechen Zhang, Aruzhan Amanbayeva Courses Algorithmic game theory MGT-435 We will study mathematical models of the interplay between algorithms and strategic behavior. We cover fundamental concepts from game theory and mechanism design, including Nash equilibria, the price of anarchy, auctions and market design, incentive compatibility, and online learn

Official source

https://edu.epfl.ch/coursebook/en/applied-probability-stochastic-processes-MGT-484

Moodle Page

http://go.epfl.ch/MGT-484

About this result

Lectures in this course (23)

Markov Chains: Theory and Applications

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

Applied Probability and Stochastic Processes: Tutorial 02

Covers communicating classes, hitting time, probabilities, and the Gambler's Ruin Model in stochastic processes.

Birth & Death Chains: Analysis & Probabilities

Explores birth and death chains, hitting probabilities, and expected game durations in Markov chains.

Recurrence and Transience: Markov Chains

Explores recurrence and transience in Markov chains, discussing the strong Markov property and state classifications.

Invariant Distributions: Markov Chains

Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.

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