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Critical Point

March 23, 2013

In my last post, I mentioned that I was trying to estimate the critical temperature of the Potts model by simulation. As it turns out, there is an equation to calculate the critical value of β exactly for a 2D lattice. Details after the jump.

Kramers & Wannier derived the critical temperature of the Ising model on a regular 2D lattice in their 1941 paper:

\frac{J}{kT_c} = 0.8814

The book chapter of Hurn, Husby & Rue (2003; p94) states in equation (3.13) that \beta_{\textrm{critical}} = \log\left(1 + \sqrt{2}\right) = 0.881373\dots

However, this is contradicted by Marin & Robert (2007) who report a value of 2.269185

So, which is it? First of all, the parameter β is known as the inverse temperature by reference to the origins of the Potts/Ising model in statistical mechanics. The relationship between the critical temperature \mathrm{T}_c and the value of the parameter \beta_c is as follows:

\beta_c = \frac{2\mathrm{J}}{k\mathrm{T}_c}

Therefore it makes sense that the value reported by Marin & Robert happens to be 2 \times \beta_c^{-1}. Now that I’ve fixed the major bugs in my code, I’m able to verify this by simulation:

library(PottsUtils)
library(bayesImageS)

k <- 2
bcrit <- 2/log(1 + sqrt(k))
beta <- seq(0,2.5,by=0.1)
counts <- matrix(0,length(beta),1000)
neigh <- getNeighbors(matrix(1,100,100), c(2,2,0,0))
block <- getBlocks(matrix(1,100,100), 2)
for (i in 1:length(beta)) {
  print(beta[i])
  print(system.time(result <- mcmcPottsNoData(beta[i],k,neigh,block,11000)))
  counts[i,] <- result$sum[10001:11000]
}

## [1] 0
## user system elapsed
## 19.53 2.89 8.29
## [1] 0.1
## user system elapsed
## 44.36 2.35 15.79
## [1] 0.2
## user system elapsed
## 43.88 2.42 14.68
## [1] 0.3
## user system elapsed
## 41.12 2.65 14.19
## [1] 0.4
## user system elapsed
## 39.34 2.48 14.08
## [1] 0.5
## user system elapsed
## 38.47 2.45 13.16
## [1] 0.6
## user system elapsed
## 38.69 2.70 13.12
## [1] 0.7
## user system elapsed
## 35.56 2.67 12.52
## [1] 0.8
## user system elapsed
## 34.88 3.06 12.27
## [1] 0.9
## user system elapsed
## 31.38 2.28 11.45
## [1] 1
## user system elapsed
## 29.72 2.55 10.87
## [1] 1.1
## user system elapsed
## 29.73 2.27 10.73
## [1] 1.2
## user system elapsed
## 27.68 2.69 10.45
## [1] 1.3
## user system elapsed
## 28.69 2.31 11.03
## [1] 1.4
## user system elapsed
## 28.03 2.73 10.51
## [1] 1.5
## user system elapsed
## 28.07 2.78 10.55
## [1] 1.6
## user system elapsed
## 26.43 2.46 10.30
## [1] 1.7
## user system elapsed
## 27.35 2.42 10.55
## [1] 1.8
## user system elapsed
## 26.46 2.67 10.30
## [1] 1.9
## user system elapsed
## 29.42 2.29 10.50
## [1] 2
## user system elapsed
## 28.47 2.45 10.45
## [1] 2.1
## user system elapsed
## 27.98 3.12 10.38
## [1] 2.2
## user system elapsed
## 28.38 2.69 10.48
## [1] 2.3
## user system elapsed
## 29.08 2.48 10.45
## [1] 2.4
## user system elapsed
## 28.32 2.51 10.51
## [1] 2.5
## user system elapsed
## 27.68 2.59 10.54

x <- rep(beta,each=1000)
y <- as.vector(t(counts))
plot(x, y, xlab = expression(beta), ylab = "identical neighbours", main = "Critical temperature of the 2D Ising model")
abline(v = bcrit, col = "red")
abline(v = log(1 + sqrt(2)), col = "blue")

Ising critical temperature

References

Hurn M A, Husby O K & Rue H (2003) A Tutorial on Image Analysis in Møller J (ed.) Spatial Statistics and Computational Methods Lecture Notes in Statistics 173, Springer-Verlag: Berlin

Kramers H A & Wannier G H (1941) Statistics of the Two-Dimensional Ferromagnet. Part I Phys. Rev. 60 252, 263

Marin J M & Robert C P (2007) Bayesian Core: A Practical Approach to Computational Bayesian Statistics Springer-Verlag: New York

Onsager L (1944) A Two-Dimensional Model with an Order-Disorder Transition Phys. Rev. 65 117

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