@article{ghahramani_probabilistic_2015,
title = {Probabilistic machine learning and artificial intelligence},
volume = {521},
issn = {0028-0836, 1476-4687},
url = {http://www.nature.com/articles/nature14541},
doi = {10/gdxwhq},
language = {en},
number = {7553},
urldate = {2019-11-28},
journal = {Nature},
author = {Ghahramani, Zoubin},
month = may,
year = {2015},
note = {ZSCC: 0000611},
keywords = {Bayesian inference, Classical ML, Machine learning, Probabilistic programming},
pages = {452--459}
}
@article{jacobs_predicate/state_2016,
series = {The {Thirty}-second {Conference} on the {Mathematical} {Foundations} of {Programming} {Semantics} ({MFPS} {XXXII})},
title = {A {Predicate}/{State} {Transformer} {Semantics} for {Bayesian} {Learning}},
volume = {325},
issn = {1571-0661},
url = {http://www.sciencedirect.com/science/article/pii/S1571066116300883},
doi = {10/ggdgbb},
abstract = {This paper establishes a link between Bayesian inference (learning) and predicate and state transformer operations from programming semantics and logic. Specifically, a very general definition of backward inference is given via first applying a predicate transformer and then conditioning. Analogously, forward inference involves first conditioning and then applying a state transformer. These definitions are illustrated in many examples in discrete and continuous probability theory and also in quantum theory.},
language = {en},
urldate = {2019-11-24},
journal = {Electronic Notes in Theoretical Computer Science},
author = {Jacobs, Bart and Zanasi, Fabio},
month = oct,
year = {2016},
note = {ZSCC: 0000030},
keywords = {Bayesianism, Categorical ML, Categorical probability theory, Effectus theory, Programming language theory, Semantics},
pages = {185--200}
}
@article{tran_deep_2017,
title = {Deep {Probabilistic} {Programming}},
url = {http://arxiv.org/abs/1701.03757},
abstract = {We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. For efficiency, Edward is integrated into TensorFlow, providing significant speedups over existing probabilistic systems. For example, we show on a benchmark logistic regression task that Edward is at least 35x faster than Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it is as fast as handwritten TensorFlow.},
urldate = {2019-11-27},
journal = {arXiv:1701.03757 [cs, stat]},
author = {Tran, Dustin and Hoffman, Matthew D. and Saurous, Rif A. and Brevdo, Eugene and Murphy, Kevin and Blei, David M.},
month = mar,
year = {2017},
note = {ZSCC: 0000108
arXiv: 1701.03757},
keywords = {Bayesian inference, Implementation, Machine learning, Probabilistic programming}
}