Gen: a general-purpose probabilistic programming system with programmable inference
Although probabilistic programming is widely used for some restricted classes of statistical
models, existing systems lack the flexibility and efficiency needed for practical use with more …
models, existing systems lack the flexibility and efficiency needed for practical use with more …
Probabilistic programming with programmable inference
We introduce inference metaprogramming for probabilistic programming languages,
including new language constructs, a formalism, and the rst demonstration of e ectiveness in …
including new language constructs, a formalism, and the rst demonstration of e ectiveness in …
Automating involutive MCMC using probabilistic and differentiable programming
M Cusumano-Towner, AK Lew… - arXiv preprint arXiv …, 2020 - arxiv.org
Involutive MCMC is a unifying mathematical construction for MCMC kernels that generalizes
many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels …
many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels …
Divide, conquer, and combine: a new inference strategy for probabilistic programs with stochastic support
Universal probabilistic programming systems (PPSs) provide a powerful framework for
specifying rich probabilistic models. They further attempt to automate the process of drawing …
specifying rich probabilistic models. They further attempt to automate the process of drawing …
Probabilistic loop synthesis from sequences of moments
M Stankovič, E Bartocci - … on Quantitative Evaluation of Systems and …, 2024 - Springer
Probabilistic program synthesis consists in automatically creating programs generating
random values adhering to specified distributions. We consider here the family of …
random values adhering to specified distributions. We consider here the family of …
Gen: a high-level programming platform for probabilistic inference
MF Cusumano-Towner - 2020 - dspace.mit.edu
Probabilistic inference provides a powerful theoretical framework for engineering intelligent
systems. However, diverse modeling approaches and inference algorithms are needed to …
systems. However, diverse modeling approaches and inference algorithms are needed to …
Probabilistic Loop Synthesis
M Stankovič, E Bartoccid - … of Systems and Formal Modeling and …, 2024 - books.google.com
Probabilistic program synthesis consists in automatically creating programs generating
random values adhering to specified dis-tributions. We consider here the family of …
random values adhering to specified dis-tributions. We consider here the family of …
Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs
FAK Saad - 2022 - dspace.mit.edu
How can we automate and scale up the processes of learning accurate probabilistic models
of complex data and obtaining principled solutions to probabilistic inference and analysis …
of complex data and obtaining principled solutions to probabilistic inference and analysis …
Meta-learning an inference algorithm for probabilistic programs
G Che, H Yang - arXiv preprint arXiv:2103.00737, 2021 - arxiv.org
We present a meta-algorithm for learning a posterior-inference algorithm for restricted
probabilistic programs. Our meta-algorithm takes a training set of probabilistic programs that …
probabilistic programs. Our meta-algorithm takes a training set of probabilistic programs that …
Automating inference for non–standard models
Y Zhou - 2020 - ora.ox.ac.uk
Probabilistic models enable us to infer the underlying relationships within data and make
decisions based on this information. Certain models are more commonly used not because …
decisions based on this information. Certain models are more commonly used not because …