Lecture

Correlated Sampling: Weight Function and Markovian Chain

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In course

PHYS-403: Computer simulation of physical systems I

The two main topics covered by this course are classical molecular dynamics and the Monte Carlo method. ...

Description

This lecture covers the concept of correlated sampling, focusing on the correct description of the weight function and the introduction of Markovian chains. It explains the abandonment of statistical independence for calculating integrals and the method for generating random variables according to a given weight function. The theorem associated with a Markovian chain and the conditions of ergodicity are discussed, along with the notion of walkers and the scope in correlated sampling.

Instructor

Alfredo Pasquarello

Alfredo Pasquarello studied physics at the Scuola Normale Superiore of Pisa and at the University of Pisa, obtaining their respective degrees in 1986. He obtained a doctoral degree at the EPFL in 1991 with a thesis on Multiphoton Transitions in Solids . Then, he moved to Bell Laboratories at Murray Hill (New Jersey), where he carried out postdoctoral research on the magnetic properties of carbon fullerenes. In 1993, he joined the Institute for Numerical Research in the Physics of Materials (IRRMA), where his activity involved first-principles simulation methods. In 1998, he was awarded the EPFL Latsis Prize for his research work on disordered silica materials. Succeeding in grant programs of the Swiss National Science Foundation, he then set up his own research group at IRRMA. In July 2003, he is appointed Professor in Theoretical Condensed Matter Physics at EPFL. Currently, he leads the Chair of Atomic Scale Simulation.

Official source

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

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