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Construction of an irreducible Markov chain in the Ising model
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Construction of an irreducible Markov Chain is a mathematical method used to prove results related the changing of magnetic materials in the Ising model, enabling the study of phase transitions and critical phenomena.
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The Ising model, a mathematical model in statistical mechanics, is utilized to study magnetic phase transitions and is a fundamental model of interacting systems.[1] Constructing an irreducible Markov chain within a finite Ising model is essential for overcoming computational challenges encountered when achieving exact goodness-of-fit tests with Markov chain Monte Carlo (MCMC) methods.
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Markov bases
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In the context of the Ising model, a Markov basis is a set of integer vectors that enables the construction of an irreducible Markov chain. Every integer vector can be uniquely decomposed as , where and are non-negative vectors. A Markov basis satisfies the following conditions:
(i) For all , there must be and .
(ii) For any and any , there always exist satisfy:
and
for l = 1,...,k.
The element of is moved. An aperiodic, reversible, and irreducible Markov Chain can then be obtained using Metropolis–Hastings algorithm.
Persi Diaconis and Bernd Sturmfels showed that (1) a Markov basis can be defined algebraically as an Ising model[2] and (2) any generating set for the ideal , is a Markov basis for the Ising model.[3]
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Construction of an irreducible Markov Chain
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To obtain uniform samples from and avoid inaccurate p-values, it is necessary to construct an irreducible Markov chain without modifying the algorithm proposed by Diaconis and Sturmfels.
A simple swap of the form , where is the canonical basis vector, changes the states of two lattice points in y. The set Z denotes the collection of simple swaps. Two configurations are -connected by Z if there exists a path between y and y′ consisting of simple swaps .
The algorithm proceeds as follows:
with
for
The algorithm can now be described as:
(i) Start with the Markov chain in a configuration
(ii) Select at random and let .
(iii) Accept if ; otherwise remain in y.
Although the resulting Markov Chain possibly cannot leave the initial state, the problem does not arise for a 1-dimensional Ising model. In higher dimensions, this problem can be overcome by using the Metropolis-Hastings algorithm in the smallest expanded sample space .[4]
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Irreducibility in the 1-Dimensional Ising Model
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The proof of irreducibility in the 1-dimensional Ising model requires two lemmas.
Lemma 1: The max-singleton configuration of for the 1-dimension Ising model is unique (up to location of its connected components) and consists of singletons and one connected component of size .
Lemma 2: For and , let denote the unique max-singleton configuration. There exists a sequence such that:
and
for
Since is the smallest expanded sample space which contains , any two configurations in can be connected by simple swaps Z without leaving . This is proved by Lemma 2, so one can achieve the irreducibility of a Markov chain based on simple swaps for the 1-dimension Ising model.[5]
It is also possible to get the same conclusion for a dimension 2 or higher Ising model using the same steps outlined above.
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References
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