Multivariate binary probability distribution in the Grassmann formalism
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We propose a probability distribution for multivariate binary random variables. For this purpose, we use the Grassmann number, an anti-commuting number. In our model, the partition function, the central moment, and the marginal and conditional distributions are expressed analytically by the matrix of the parameters analogous to the covariance matrix in the multivariate Gaussian distribution. That is, summation over all possible states is not necessary for obtaining the partition function and various expected values, which is a problem with the conventional multivariate Bernoulli distribution. The proposed model has many similarities to the multivariate Gaussian distribution. For example, the marginal and conditional distributions are expressed by the parameter matrix and its inverse matrix, respectively. That is, the inverse matrix expresses a sort of partial correlation. Analytical expressions for the marginal and conditional distributions are also useful in generating random numbers for multivariate binary variables. Hence, we validated the proposed method using synthetic datasets. We observed that the sampling distributions of various statistics are consistent with the theoretical predictions and estimates are consistent and asymptotically normal.
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