Computer Science
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CS 263 Counting and Sampling

Autumn 2022

Stanford University

The course addresses both classic and recent developments in counting and sampling. It covers counting complexity, exact counting via determinants, sampling via Markov chains, and high-dimensional expanders.

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Overview

We will cover classic topics as well as some recent developments in counting and sampling.

Classic topics include:

  • Counting complexity and #P
  • Self-reducibility, reductions between counting and sampling
  • Exact counting via determinants: spanning trees, planar perfect matchings
  • Sampling via Markov chains
  • Probabilistic analysis of Markov chains: stationary times, path coupling, coupling from the past
  • Functional analysis of Markov chains: spectral gap, Poincare and log-Sobolev inequalities, conductance, canonical flows
  • Deterministic counting: correlation decay, zeros of polynomials, Barvinok’s method, variational methods

Recent devlopments, focusing on an emerging high-dimensional-expanders-based lens, include:

  • Trickle down and and the local-to-global method
  • Matroids and log-concave polynomials
  • Spectral independence, entropic independence, entropy factorization
  • Establishing high-dimensional expansion from coupling, correlation decay, zero-freeness
  • Stochastic localization

Prerequisites

No data.

Learning objectives

No data.

Textbooks and other notes

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Courseware availability

Lecture slides available at Lectures

No videos available

Homework available at Homework

Resources available at Resources

Covered concepts