Gibbs algorithm - Revision history

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2024-03-13T03:50:34Z

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	{{short description\|Criterion for choosing a probability distribution in statistical mechanics}}		{{short description\|Criterion for choosing a probability distribution in statistical mechanics}}
	{{Distinguish \|Gibbs sampler}}		{{Distinguish \|Gibbs sampler}}
			{{one source \|date=March 2024}}
	[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]		[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]
	In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability

Fadesga: /* References */

2022-07-19T11:59:04Z

References

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	[[Category:Particle statistics]]		[[Category:Particle statistics]]
	[[Category:Entropy and information]]		[[Category:Entropy and information]]


			{{statisticalmechanics-stub}}

MaxwellMolecule: Undid revision 1063293845 by 49.206.1.168 (talk)

2022-01-02T18:31:10Z

Undid revision 1063293845 by 49.206.1.168 (talk)

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	{{Distinguish \|Gibbs sampler}}		{{Distinguish \|Gibbs sampler}}
	[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]		[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]
	In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by ~~min,mjnbhjbimizing~~ the average log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability

	:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>		:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>

49.206.1.168: hj

2022-01-02T06:17:57Z

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	{{Distinguish \|Gibbs sampler}}		{{Distinguish \|Gibbs sampler}}
	[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]		[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]
	In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by ~~minimizing~~ the average log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by min,mjnbhjbimizing the average log probability

	:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>		:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>

Footlessmouse: Importing Wikidata short description: "Criterion for choosing a probability distribution in statistical mechanics" (Shortdesc helper)

2020-11-28T22:07:56Z

Importing Wikidata short description: "Criterion for choosing a probability distribution in statistical mechanics" (Shortdesc helper)

← Previous revision		Revision as of 22:07, 28 November 2020
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			{{short description\|Criterion for choosing a probability distribution in statistical mechanics}}
	{{Distinguish \|Gibbs sampler}}		{{Distinguish \|Gibbs sampler}}
	[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]		[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]

Citation bot: Alter: pages. Add: series. Formatted dashes. | You can use this bot yourself. Report bugs here. | Suggested by Abductive | Category:Statistical mechanics | via #UCB_Category 221/273

2020-10-16T03:37:07Z

Alter: pages. Add: series. Formatted dashes. | You can use this bot yourself. Report bugs here. | Suggested by Abductive | Category:Statistical mechanics | via #UCB_Category 221/273

← Previous revision		Revision as of 03:37, 16 October 2020
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	:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>		:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>

	subject to the probability distribution {{math\|''p<sub>i</sub>''}} satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities.<ref name=Dewar>{{cite book\|first=Roderick C. \|last=Dewar\|chapter=4. Maximum Entropy Production and Non-equilibrium Statistical Mechanics\|editor-last1=Kleidon\|editor-first1=A.\|title=Non-equilibrium thermodynamics and the production of entropy : life, earth, and beyond\|url=https://archive.org/details/nonequilibriumth00klei \|url-access=limited \|date=2005\|publisher=Springer\|location=Berlin\|isbn=9783540224952\|pages=~~[https://archive.org/details/nonequilibriumth00klei/page/n57 41]–55~~\|doi=10.1007/11672906_4}}</ref> in 1948, [[Claude E. Shannon\|Claude Shannon]] interpreted the negative of this quantity, which he called [[entropy_(Information_theory)\|information entropy]], as a measure of the uncertainty in a probability distribution.<ref name=Dewar/> In 1957, [[E.T. Jaynes]] realized that this quantity could be interpreted as missing information about anything, and generalized the Gibbs algorithm to non-equilibrium systems with the [[principle of maximum entropy]] and [[maximum entropy thermodynamics]].<ref name=Dewar/>		subject to the probability distribution {{math\|''p<sub>i</sub>''}} satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities.<ref name=Dewar>{{cite book\|first=Roderick C. \|last=Dewar\|chapter=4. Maximum Entropy Production and Non-equilibrium Statistical Mechanics\|editor-last1=Kleidon\|editor-first1=A.\|title=Non-equilibrium thermodynamics and the production of entropy : life, earth, and beyond\|series=Understanding Complex Systems\|url=https://archive.org/details/nonequilibriumth00klei \|url-access=limited \|date=2005\|publisher=Springer\|location=Berlin\|isbn=9783540224952\|pages=41–55\|doi=10.1007/11672906_4}}</ref> in 1948, [[Claude E. Shannon\|Claude Shannon]] interpreted the negative of this quantity, which he called [[entropy_(Information_theory)\|information entropy]], as a measure of the uncertainty in a probability distribution.<ref name=Dewar/> In 1957, [[E.T. Jaynes]] realized that this quantity could be interpreted as missing information about anything, and generalized the Gibbs algorithm to non-equilibrium systems with the [[principle of maximum entropy]] and [[maximum entropy thermodynamics]].<ref name=Dewar/>

	Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').		Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').

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2020-05-31T10:30:54Z

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← Previous revision		Revision as of 10:30, 31 May 2020
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	:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>		:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>

	subject to the probability distribution {{math\|''p<sub>i</sub>''}} satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities.<ref name=Dewar>{{cite book\|first=Roderick C. \|last=Dewar\|chapter=4. Maximum Entropy Production and Non-equilibrium Statistical Mechanics\|editor-last1=Kleidon\|editor-first1=A.\|title=Non-equilibrium thermodynamics and the production of entropy : life, earth, and beyond\|date=2005\|publisher=Springer\|location=Berlin\|isbn=9783540224952\|pages=41–55\|doi=10.1007/11672906_4}}</ref> in 1948, [[Claude E. Shannon\|Claude Shannon]] interpreted the negative of this quantity, which he called [[entropy_(Information_theory)\|information entropy]], as a measure of the uncertainty in a probability distribution.<ref name=Dewar/> In 1957, [[E.T. Jaynes]] realized that this quantity could be interpreted as missing information about anything, and generalized the Gibbs algorithm to non-equilibrium systems with the [[principle of maximum entropy]] and [[maximum entropy thermodynamics]].<ref name=Dewar/>		subject to the probability distribution {{math\|''p<sub>i</sub>''}} satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities.<ref name=Dewar>{{cite book\|first=Roderick C. \|last=Dewar\|chapter=4. Maximum Entropy Production and Non-equilibrium Statistical Mechanics\|editor-last1=Kleidon\|editor-first1=A.\|title=Non-equilibrium thermodynamics and the production of entropy : life, earth, and beyond\|url=https://archive.org/details/nonequilibriumth00klei \|url-access=limited \|date=2005\|publisher=Springer\|location=Berlin\|isbn=9783540224952\|pages=[https://archive.org/details/nonequilibriumth00klei/page/n57 41]–55\|doi=10.1007/11672906_4}}</ref> in 1948, [[Claude E. Shannon\|Claude Shannon]] interpreted the negative of this quantity, which he called [[entropy_(Information_theory)\|information entropy]], as a measure of the uncertainty in a probability distribution.<ref name=Dewar/> In 1957, [[E.T. Jaynes]] realized that this quantity could be interpreted as missing information about anything, and generalized the Gibbs algorithm to non-equilibrium systems with the [[principle of maximum entropy]] and [[maximum entropy thermodynamics]].<ref name=Dewar/>

	Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').		Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').

老陳 at 05:15, 30 April 2016

2016-04-30T05:15:10Z

← Previous revision		Revision as of 05:15, 30 April 2016
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	{{Distinguish \|Gibbs sampler}}		{{Distinguish \|Gibbs sampler}}
			[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]

	In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability

RockMagnetist: Added citations, removed tags, added Distinguish hatnote

2015-09-13T06:53:00Z

Added citations, removed tags, added Distinguish hatnote

← Previous revision		Revision as of 06:53, 13 September 2015
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			{{Distinguish \|Gibbs sampler}}
	{{Unreferenced\|date=December 2009}}
	{{Disputed\|date=March 2008}}

	In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in ~~1878~~, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by ~~maximising~~ the average ~~negative~~ log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability
	(or [[entropy_(Information_theory)\|information-theoretic entropy]])

	:<math> H = \sum_i -p_i \ln p_i \, </math>		:<math> \langle\ln p_i\rangle = \sum_i p_i \ln p_i \, </math>

	subject to the probability distribution ''~~p_i~~'' satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities. ~~Physicists~~ ~~call~~ the ~~result~~ of ~~applying~~ ~~the~~ ~~Gibbs~~ ~~algorithm~~ ~~the~~ [[~~Gibbs~~ ~~distribution~~]] ~~for~~ the ~~given~~ ~~constraints~~, ~~most~~ ~~notably~~ ~~Gibbs's~~ [[~~grand canonical~~ ~~ensemble~~]] ~~for~~ ~~open~~ ~~systems~~ ~~when~~ the ~~average~~ ~~energy~~ and the ~~average~~ ~~number~~ of ~~particles~~ ~~are~~ ~~given.~~ ~~(See~~ ~~also ''~~[[~~Partition~~ ~~function~~ ~~(mathematics)\|partition~~ ~~function~~]]~~'')~~.		subject to the probability distribution {{math\|''p<sub>i</sub>''}} satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities.<ref name=Dewar>{{cite book\|first=Roderick C. \|last=Dewar\|chapter=4. Maximum Entropy Production and Non-equilibrium Statistical Mechanics\|editor-last1=Kleidon\|editor-first1=A.\|title=Non-equilibrium thermodynamics and the production of entropy : life, earth, and beyond\|date=2005\|publisher=Springer\|location=Berlin\|isbn=9783540224952\|pages=41–55\|doi=10.1007/11672906_4}}</ref> in 1948, [[Claude E. Shannon\|Claude Shannon]] interpreted the negative of this quantity, which he called [[entropy_(Information_theory)\|information entropy]], as a measure of the uncertainty in a probability distribution.<ref name=Dewar/> In 1957, [[E.T. Jaynes]] realized that this quantity could be interpreted as missing information about anything, and generalized the Gibbs algorithm to non-equilibrium systems with the [[principle of maximum entropy]] and [[maximum entropy thermodynamics]].<ref name=Dewar/>

			Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').
	In the light of [[Claude E. Shannon\|Claude Shannon]]'s [[information theory]], in 1957 [[E.T. Jaynes]] re-interpreted the Gibbs algorithm as a much more general, more widely applicable inference technique, leading to the [[principle of maximum entropy]], and the [[Maximum entropy thermodynamics\|MaxEnt view of thermodynamics]].

	This general result of the Gibbs algorithm is then a [[maximum entropy probability distribution]]. Statisticians identify such distributions as belonging to [[exponential family\|exponential families]].		This general result of the Gibbs algorithm is then a [[maximum entropy probability distribution]]. Statisticians identify such distributions as belonging to [[exponential family\|exponential families]].

			==References==
	==Not to be confused with==
			{{Reflist}}
	The [[Gibbs sampler]], an update algorithm used in [[Markov chain Monte Carlo]] iterations, a special case of the [[Metropolis-Hastings algorithm]].

	{{DEFAULTSORT:Gibbs Algorithm}}		{{DEFAULTSORT:Gibbs Algorithm}}

89.217.13.75: Rewrote lede to be slightly clearer

2015-02-03T22:34:25Z

Rewrote lede to be slightly clearer

← Previous revision		Revision as of 22:34, 3 February 2015
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	{{Disputed\|date=March 2008}}		{{Disputed\|date=March 2008}}

	In [[statistical mechanics]], the '''Gibbs algorithm''', ~~first~~ introduced by [[J. Willard Gibbs]] in 1878, is ~~the~~ ~~injunction~~ to ~~choose~~ a [[~~statistical~~ ~~ensemble~~]] ~~(probability distribution)~~ for the ~~unknown~~ [[microstate (statistical mechanics)\|~~microscopic state~~]] of a [[thermodynamic system]] by ~~minimising~~ the average log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1878, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by maximising the average negative log probability
			(or [[entropy_(Information_theory)\|information-theoretic entropy]])

	:<math> H = \sum_i -p_i \ln p_i \, </math>		:<math> H = \sum_i -p_i \ln p_i \, </math>

	subject to the probability distribution satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities. Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').		subject to the probability distribution ''p_i'' satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities. Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').

	In the light of [[Claude E. Shannon\|Claude Shannon]]'s [[information theory]], in 1957 [[E.T. Jaynes]] re-interpreted the Gibbs algorithm as a much more general, more widely applicable inference technique, leading to the [[principle of maximum entropy]], and the [[Maximum entropy thermodynamics\|MaxEnt view of thermodynamics]].		In the light of [[Claude E. Shannon\|Claude Shannon]]'s [[information theory]], in 1957 [[E.T. Jaynes]] re-interpreted the Gibbs algorithm as a much more general, more widely applicable inference technique, leading to the [[principle of maximum entropy]], and the [[Maximum entropy thermodynamics\|MaxEnt view of thermodynamics]].

← Previous revision		Revision as of 03:50, 13 March 2024
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	{{short description\|Criterion for choosing a probability distribution in statistical mechanics}}		{{short description\|Criterion for choosing a probability distribution in statistical mechanics}}
	{{Distinguish \|Gibbs sampler}}		{{Distinguish \|Gibbs sampler}}
			{{one source \|date=March 2024}}
	[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]		[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]
	In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability

← Previous revision		Revision as of 22:07, 28 November 2020
Line 1:		Line 1:
			{{short description\|Criterion for choosing a probability distribution in statistical mechanics}}
	{{Distinguish \|Gibbs sampler}}		{{Distinguish \|Gibbs sampler}}
	[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]		[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]

← Previous revision		Revision as of 05:15, 30 April 2016
Line 1:		Line 1:
	{{Distinguish \|Gibbs sampler}}		{{Distinguish \|Gibbs sampler}}
			[[FILE:Josiah Willard Gibbs -from MMS-.jpg\|thumb\|200px\|Josiah Willard Gibbs]]

	In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1902, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by minimizing the average log probability

← Previous revision		Revision as of 22:34, 3 February 2015
Line 2:		Line 2:
	{{Disputed\|date=March 2008}}		{{Disputed\|date=March 2008}}

	In [[statistical mechanics]], the '''Gibbs algorithm''', ~~first~~ introduced by [[J. Willard Gibbs]] in 1878, is ~~the~~ ~~injunction~~ to ~~choose~~ a [[~~statistical~~ ~~ensemble~~]] ~~(probability distribution)~~ for the ~~unknown~~ [[microstate (statistical mechanics)\|~~microscopic state~~]] of a [[thermodynamic system]] by ~~minimising~~ the average log probability		In [[statistical mechanics]], the '''Gibbs algorithm''', introduced by [[J. Willard Gibbs]] in 1878, is a criterion for choosing a [[probability distribution]] for the [[statistical ensemble]] of [[microstate (statistical mechanics)\|microstate]]s of a [[thermodynamic system]] by maximising the average negative log probability
			(or [[entropy_(Information_theory)\|information-theoretic entropy]])

	:<math> H = \sum_i -p_i \ln p_i \, </math>		:<math> H = \sum_i -p_i \ln p_i \, </math>

	subject to the probability distribution satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities. Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').		subject to the probability distribution ''p_i'' satisfying a set of constraints (usually expectation values) corresponding to the known [[macroscopic]] quantities. Physicists call the result of applying the Gibbs algorithm the [[Gibbs distribution]] for the given constraints, most notably Gibbs's [[grand canonical ensemble]] for open systems when the average energy and the average number of particles are given. (See also ''[[Partition function (mathematics)\|partition function]]'').

	In the light of [[Claude E. Shannon\|Claude Shannon]]'s [[information theory]], in 1957 [[E.T. Jaynes]] re-interpreted the Gibbs algorithm as a much more general, more widely applicable inference technique, leading to the [[principle of maximum entropy]], and the [[Maximum entropy thermodynamics\|MaxEnt view of thermodynamics]].		In the light of [[Claude E. Shannon\|Claude Shannon]]'s [[information theory]], in 1957 [[E.T. Jaynes]] re-interpreted the Gibbs algorithm as a much more general, more widely applicable inference technique, leading to the [[principle of maximum entropy]], and the [[Maximum entropy thermodynamics\|MaxEnt view of thermodynamics]].