Continuous Bernoulli distribution In probability theory , statistics , and machine learning , the continuous Bernoulli distribution is a family of continuous probability distributions parameterized by a single shape parameter , defined on the unit interval, by: The continuous Bernoulli distribution arises in deep learning and computer vision , specifically in the context of variational autoencoders, for modeling the pixel intensities of natural images. As such, it defines a proper probabilistic counterpart for the commonly used binary cross entropy loss, which is often applied to continuous, -valued data. This practice amounts to ignoring the normalizing constant of the continuous Bernoulli distribution, since the binary cross entropy loss only defines a true log-likelihood for discrete, -valued data. The continuous Bernoulli also defines an exponential family of distributions . Writing for the natural parameter , the density can be rewritten in canonical form:Related distributions Bernoulli distribution The continuous Bernoulli can be thought of as a continuous relaxation of the Bernoulli distribution, which is defined on the discrete set by the probability mass function: where is a scalar parameter between 0 and 1. Applying this same functional form on the continuous interval results in the continuous Bernoulli probability density function , up to a normalizing constant.The Beta distribution has the density function: which can be re-written as: where are positive scalar parameters, and represents an arbitrary point inside the 1-simplex,. Switching the role of the parameter and the argument in this density function , we obtain: This family is only identifiable up to the linear constraint , whence we obtain: corresponding exactly to the continuous Bernoulli density.Exponential distribution An exponential distribution restricted to the unit interval is equivalent to a continuous Bernoulli distribution with appropriate parameter.The multivariate generalization of the continuous Bernoulli is called the continuous categorical .
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