Essential Reading - Revision history

Tony at 00:36, 18 July 2009

2009-07-18T00:36:23Z

← Older revision		Revision as of 00:28, 18 July 2009
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	Understand the relation of new work-based objectives to classical unsupervised learning objectives like		Understand the relation of new work-based objectives to classical unsupervised learning objectives like
	maximum likelihood.		maximum likelihood.


	PAPERS:		PAPERS:

Tony at 00:34, 18 July 2009

2009-07-18T00:34:48Z

← Older revision		Revision as of 00:26, 18 July 2009
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	Crooks G.E. 2007. Beyond Boltzmann-Gibbs statistics: Maximum entropy hyper-ensembles out of equilibrium		Crooks G.E. 2007. Beyond Boltzmann-Gibbs statistics: Maximum entropy hyper-ensembles out of equilibrium


	The following 3 papers are very insightful and contain some of the most advanced ideas		The following 3 papers are very insightful and contain some of the most advanced ideas
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	Parrondo J.M.R., Van den Broeck \& Kawai R. 2009. Entropy production and the arrow of time		Parrondo J.M.R., Van den Broeck \& Kawai R. 2009. Entropy production and the arrow of time


	Mae's papers are really difficult and confusing but they seem to be the most advanced work.		Mae's papers are really difficult and confusing but they seem to be the most advanced work.
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	Maes C. 2001. Statistical mechanics of entropy production: Gibbsian hypothesis and local fluctuations		Maes C. 2001. Statistical mechanics of entropy production: Gibbsian hypothesis and local fluctuations


	Kurchan J. 2005. Non-equilibrium work relations		Kurchan J. 2005. Non-equilibrium work relations

Tony at 00:32, 18 July 2009

2009-07-18T00:32:55Z

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	2. Connect these to machine learning of time series via objectives like Minimum Conditional Entropy Production.		2. Connect these to machine learning of time series via objectives like Minimum Conditional Entropy Production.
			Understand the relation of new work-based objectives to classical unsupervised learning objectives like
			maximum likelihood.

	PAPERS:		PAPERS:

Tony at 00:20, 18 July 2009

2009-07-18T00:20:58Z

New page

GOALS:

1. Understand the relations between entropy production, free energy changes and work done in the
non-equilibrium regime of Markov processes and deterministic Hamiltonian dynamics. ie: understand
all the different Fluctuation Theorems.

2. Connect these to machine learning of time series via objectives like Minimum Conditional Entropy Production.

PAPERS:

These are the most important papers from Crooks.
His thesis goes through the theorems for Markov chains thoroughly. You will find it on his webpage
www.threeplusone.com

Crooks G.E. 1999. Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences

Crooks G.E. 1999. Path ensemble averages in systems driven far from equilibrium.

Crooks G.E. 2007. Beyond Boltzmann-Gibbs statistics: Maximum entropy hyper-ensembles out of equilibrium

The following 3 papers are very insightful and contain some of the most advanced ideas

Gomez-Martin A., Parrondo J.M.R. \& Van den Broeck. 2008. The "footprints" of irreversibilty

Kawai R., Parrondo J.M.R. \& Van den Broeck. 2007. Dissipation: the phase-space perspective

Parrondo J.M.R., Van den Broeck \& Kawai R. 2009. Entropy production and the arrow of time

Mae's papers are really difficult and confusing but they seem to be the most advanced work.
This one contains the path ensemble maths for markov chains.

Maes C. \& Netocny 2002. Time-reversal and entropy

This one you should read section 4 on Entropy Production and maybe sections 1 and 2 for background

Maes C. 2001. Statistical mechanics of entropy production: Gibbsian hypothesis and local fluctuations

Kurchan J. 2005. Non-equilibrium work relations

This is a useful short review of foundations. Not deep, but a good starting point if you're confused.

Karevski D. 2007. Foundations of statistical mechanics: in and out of equilibrium