A spatial lattice model for binary data is constructed from two spatial scales linked through conditional probabilities. A coarse grid of lattice locations is specified and all remaining locations (which we call the background) capture fine-scale spatial dependence. Binary data on the coarse grid are modelled with an autologistic distribution, conditional on the binary process on the background...

We address the problem of interactive feature construction and denoising of binary data. To this end, we develop a variational ICA method, employing a multivariate Bernoulli likelihood and independent Beta source densities. We relate this to other binary data models, demonstrating its advantages in two application domains.

Simulating spatial correlated binary data is very important on many cases, but it is not easily to accomplish, as there are restrictions on the parameters of Bernoulli variables. This paper develops a copulas method to generate spatial correlated binary data. The spatial binary data generated by this method has an inverse spatial pattern comparing with the latent Gaussian random field data, how...

Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of 1/0, with 1 generally indicating a success and 0 a failure. However, the actual values that 1 and 0 can take vary widely, depending on the purpose of the study. For example, for a study of the odds of failure in a school setting, 1 may have the value of f...

In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes. This distribution is discussed in the framework of the exponential family, and its statistical properties regarding independence of the nodes are demonstrated. Importantly the model can estimate not only the main effects and pairwise interactions among the nodes b...

The classical approach for specifying statistical models with binary dependent variables in econometrics using latent variables or threshold models can leave the model misspecified, resulting in biased and inconsistent estimates as well as erroneous inferences. Furthermore, methods for trying to alleviate such problems, such as univariate generalized linear models, have not provided an adequate...

In this paper we analyse a simple model of a digital communications channel. This model proves to be closely related to an iterated function system (IFS) related to the well-known Bernoulli convolution. We derive it from a randomly forced first-order ordinary differential equation. This allows the parameter of the Bernoulli convolution—the contraction rate, λ—to be related to the rate at which ...

This document explores the Bernoulli operator, giving it a variety of different definitions. In one definition, it is the shift operator acting on infinite strings of binary digits. In another definition, it is the transfer operator (the Frobenius-Perron operator) of the Bernoulli map, also variously known as the doubling map or the sawtooth map. The map is interesting for multiple reasons. One...

The density classification task is to determine which of the symbols appearing in an array has the majority. A cellular automaton solving this task is required to converge to a uniform configuration with the majority symbol at each site. It is not known whether a one-dimensional cellular automaton with binary alphabet can classify all Bernoulli random configurations almost surely according to t...