… For our purpose, we use a generalized notion of sampling function which maps (0.0, 1.0] ∞
… our probabilistic language λ . We begin by explaining why we choose sampling functions as …

… This article presents a probabilistic language, called λ , whose … use of sampling functions,
that is, mappings from the unit interval (0.0, 1.0] to probability domains, in specifying probability …

… to redistribute the probability mass of the sampled points in … the order conditional probabilities
of samples from our model … Instead, the values of the energy function for sampled words …

… (0.0,1.0] to a probability domain D. Given a random … a sample in D, and thus specifies a
unique probability distribution. For our purpose, we use a generalized notion of sampling function …

… probabilistic programming language for specifying statistical models. A Stan program
imperatively defines a log probability function … support sampling and optimization-based inference …

… We report on experiments using neural networks for the probability function, showing on
two text corpora that the proposed approach significantly improves on state-of-the-art n-gram …

… to importance sampling for efficient training of neural language … use probability mass functions
instead of density functions. … between samples from the data distribution and samples from …

… sampling is that we no more need to compute the partition function: we just need to compute
the energy function for the sampled points. The procedure is summarized in Algorithm 2. …

… The most notable feature of our calculus is that it is founded upon sampling functions,
which map the unit interval to probability domains. As a consequence, we achieve a unified …

… To achieve this, the sampling function now takes a third argument which is an observation
on the expression. The process of passing observations to subexpressions is simple. For …