I. INTRODUCTION Choose a graph, e.g. a square lattice, on n vertices and compute its Ising partition function Z , keeping track of its terms according to their magnetisation, so that Z = Z $_{0}$+ Z $_{1}$+ · · · + Z $_{n}$. The quotient Z $_{k}$/ Z is the probability of having k negative spins, or magnetisation M = n − 2 k . The controlling parameter of the partition function is the temperature, that is, for any given temperature the partition function provides us with a distribution of magnetisations. At infinite temperature this is simply the binomial distribution. At zero temperature, on the other hand, we receive a distribution with two peaks, one at k = 0 and one at k = n , both with 50% of the probability mass. The distribution is always symmetrical. What happens between these two extreme temperatures? Though there are exceptions to the rule, for most graphs the distribution begins its life at high temperatures as a unimodal distribution with the peak at the middle k = n/ 2. As we lower the temperature the distribution gets increasingly wider until we reach a temperature where the distribution changes from unimodal to bimodal. Near this temperature, slightly above and slightly below, the distribution is particularly wide. Lowering the temperature even further the distribution develops two sharp peaks, both essentially gaussian, and the peaks move outwards. This article tries to model the distributions using p, q -binomial coefficients. In one case, the complete graph, they model exactly the distributions and for d -dimensional lattices the dimension seems to determine how well they fit to the Ising distributions. Especially in the case of d = 1 and d = 5 the Ising distributions are particularly well-fitted by the p, q -binomial coefficients. The paper begins in section II by providing the necessary basic tools, such as the Pochhammer and q -Pochhammer symbol, q -binomial coefficients and finally the p, q -binomial coefficients. Some nice results on their properties are also stated, even though they are of no direct use to us in the rest of the paper. They are merely intended to give the reader a feel of how p, q -binomials behave. In section III we define the p, q -binomial distribution and give an algorithm for finding values of p and q when the distribution is given as input. The problem is to determine an optimal choice for p and q . As it turns out we only have to focus on the value of the probability and the location of the distribution’s peaks, at least for a bimodal distribution. The unimodal distribution always has its peak at the middle so in this case we instead take the quotient between the two middle probabilities as controlling 2