… of a practical DSL for probabilistic programming. We achieve a very shallow embedding of
the … techniques for eliminating notational and interpretive overhead in embedded languages. …

The replication crisis in empirical research calls for a more mindful approach to how we apply
and report statistical models. For empirical research to have a lasting (policy) impact, these …

… probabilistic and differentiable programming. We begin with a discussion of gradient-based
inference methods for higher-order programs … between probabilistic programming and deep …

… With inference compositionality (which we discuss in Section 4), we can embed it into more
complicated algorithms, such as with expressive variational approximations (Rezende & …

… ship between probabilistic programs and other probabilistic … models can be encoded as
probabilistic programs. In Section 4, … embedded DTMC, and encoded as a probabilistic program, …

… Our work differs from Anglican by embedding a probabilistic programming language in
Haskell and implementing inference as a deterministic data structure transformation. …

… languages are implemented as interpreters [6, 9], embedded … Anglican is implemented
using a combination of embedding … between probabilistic programs and inference algorithms …

… of programming languages, and by applying general-purpose algorithms to reason about
models. We present a new probabilistic programming … Thus, the embedding in Scala achieves …

… We propose design guidelines for a probabilistic programming … Infergo, a probabilistic
programming facility for Go, a modern … languages are often implemented as embedded domain-…

… probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for
general-purpose use. Like Church, probabilistic … ideas from probabilistic graphical models. …