The Markov chain Monte Carlo revolution

By Persi Diaconis. 2008

318 citations recorded by the source corpus.

The use of simulation for high dimensional intractable computations has revolutionized applied mathematics. Designing, improving and understanding the new tools leads to (and leans on) fascinating mathematics, from representation theory through micro-local analysis.

Subfields: Condensed Matter Physics, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4e6258ebd07b548f77f8cd33caf72d811c2bd54c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Condensed Matter Physics

1. Chuan Guo
2. Kilian Q. Weinberger
3. Ruihan Wu

In Mathematical Physics

1. Chuan Guo
2. Kilian Q. Weinberger
3. Ruihan Wu

In Statistics and Probability

1. A. Aiken
2. Chuan Guo
3. Kilian Q. Weinberger
4. Ruihan Wu

Citations in the corpus

• TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks (2020, methodology): “While strategies such as Markov Chain Monte Carlo have been used in similar contexts to efficiently reduce the search space (Diaconis, 2009), the number of candidate permutations may be too large in our case.”
• Geometric ergodicity and the spectral gap of non-reversible Markov chains (2009, unknown): “, [16, 22, 21, 8, 5, 12, 11, 13, 23, 19, 3, 2, 17, 14, 7, 6], and the relevant references therein.”
• Stochastic superoptimization (2012, background): “Successful applications are many, and include protein alignment [16], code breaking [6], and scene modeling and rendering in computer graphics [5, 19].”