
Datadriven Sequential Monte Carlo in Probabilistic Programming
Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)...
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Further Inference on Categorical Data – A Bayesian Approach
Three different inferential problems related to a two dimensional catego...
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Blang: Bayesian declarative modelling of arbitrary data structures
Consider a Bayesian inference problem where a variable of interest does ...
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Inferring Signaling Pathways with Probabilistic Programming
Cells regulate themselves via dizzyingly complex biochemical processes c...
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Cultural evolution in Vietnam's early 20th century: a Bayesian networks analysis of FrancoChinese house designs
The study of cultural evolution has taken on an increasingly interdiscip...
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LFPPL: A LowLevel First Order Probabilistic Programming Language for NonDifferentiable Models
We develop a new Lowlevel, Firstorder Probabilistic Programming Langua...
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PClean: Bayesian Data Cleaning at Scale with DomainSpecific Probabilistic Programming
Data cleaning is naturally framed as probabilistic inference in a genera...
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Functional probabilistic programming for scalable Bayesian modelling
Bayesian inference involves the specification of a statistical model by a statistician or practitioner, with careful thought about what each parameter represents. This results in particularly interpretable models which can be used to explain relationships present in the observed data. Bayesian models are useful when an experiment has only a small number of observations and in applications where transparency of data driven decisions is important. Traditionally, parameter inference in Bayesian statistics has involved constructing bespoke MCMC (Markov chain Monte Carlo) schemes for each newly proposed statistical model. This results in plausible models not being considered since efficient inference schemes are challenging to develop or implement. Probabilistic programming aims to reduce the barrier to performing Bayesian inference by developing a domain specific language (DSL) for model specification which is decoupled from the parameter inference algorithms. This paper introduces functional programming principles which can be used to develop an embedded probabilistic programming language. Model inference can be carried out using any generic inference algorithm. In this paper Hamiltonian Monte Carlo (HMC) is used, an efficient MCMC method requiring the gradient of the unnormalised logposterior, calculated using automatic differentiation. The concepts are illustrated using the Scala programming language.
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