Random Walk - Simplest example of a Markov Process
3
Continuous space time limit
4
Space Discrete/Time Continuous
5
Probability that Walker is at
6
Evolution Equation for PX,n
7
Solution of the evolution equations
8
Homework: Try solving by double fourier transform
9
Discrete time/Space Case
10
Assignments
11
Time is Continuous
12
Master equation
Description:
Explore the fundamentals of statistical physics in this preparatory lecture by Abhishek Dhar from the International Centre for Theoretical Sciences. Delve into the concept of random walks as the simplest example of a Markov process. Examine continuous space-time limits, discrete space/continuous time scenarios, and probability distributions for walker positions. Learn about evolution equations for probability functions and their solutions. Investigate discrete time/space cases, tackle assignments, and understand the concept of continuous time in relation to random walks. Conclude with an introduction to the master equation, providing a solid foundation for advanced topics in statistical physics.
Introduction to Random Walks and Markov Processes - Preparatory Lecture 1