Abstract
We present Hakaru, a new probabilistic programming system that allows composable reuse of distributions, queries, and inference algorithms, all expressed in a single language of measures. The system implements two automatic and semantics-preserving program transformations—disintegration, which calculates conditional distributions, and simplification, which subsumes exact inference by computer algebra. We show how these features work together by describing the ideal workflow of a Hakaru user on two small problems. We highlight our composition of transformations and types in design and implementation.
Thanks to Mike Kucera and Natalie Perna for helping to develop Hakaru.
This research was supported by DARPA grant FA8750-14-2-0007, NSF grant CNS-0723054, Lilly Endowment, Inc. (through its support for the Indiana University Pervasive Technology Institute), and the Indiana METACyt Initiative. The Indiana METACyt Initiative at IU is also supported in part by Lilly Endowment, Inc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We implement this universe of types by defining our own Haskell kind using GHC’s -XDataKinds extension. Thus, we call Hakaru and * both “universes” and “kinds”.
References
Carette, J., Kiselyov, O., Shan, C.-c.: Finally tagless, partially evaluated: Tagless staged interpreters for simpler typed languages. J. Funct. Program. 19(5), 509–543 (2009)
Carette, J., Shan, C.-c.: Simplifying probabilistic programs using computer algebra (2015). http://www.cs.indiana.edu/ftp/techreports/TR719.pdf
Giry, M.: A categorical approach to probability theory. In: Banaschewski, B. (ed.) Categorical Aspects of Topology and Analysis. Lecture Notes in Mathematics, vol. 915, pp. 68–85. Springer, Heidelberg (1982)
Goodman, N.D., Mansinghka, V.K., Roy, D., Bonawitz, K., Tenenbaum, J.B.: Church: A language for generative models. In: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, pp. 220–229. AUAI Press (2008)
Goodman, N.D., StuhlmĂĽller, A.: The design and implementation of probabilistic programming languages. http://dippl.org (2014). Accessed 20 November 2015
Kiselyov, O., Shan, C.-c.: Embedded probabilistic programming. In: Taha, W.M. (ed.) DSL 2009. LNCS, vol. 5658, pp. 360–384. Springer, Heidelberg (2009)
MacKay, D.J.C.: Introduction to Monte Carlo methods. In: Jordan, M.I. (ed.): Learning and Inference in Graphical Models. Kluwer (1998)
Mansinghka, V.K., Selsam, D., Perov, Y.N.: Venture: a higher-order probabilistic programming platform with programmable inference. CoRR abs/1404.0099 (2014). http://arxiv.org/abs/org/abs/1404.0099
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988). revised 2nd printing (1998)
Ramsey, N., Pfeffer, A.: Stochastic lambda calculus and monads of probability distributions. In: Conference Record of the Annual ACM Symposium on Principles of Programming Languages POPL 2002, pp. 154–165. ACM Press (2002)
Ścibior, A., Ghahramani, Z., Gordon, A.D.: Practical probabilistic programming with monads. In: Proceedings of the 8th ACM SIGPLAN Symposium on Haskell, pp. 165–176. ACM (2015)
Shan, C.-c., Ramsey, N.: Symbolic Bayesian inference by lazy partial evaluation (2015). http://www.cs.tufts.edu/~nr/pubs/disintegrator-abstract.html
Wood, F., van de Meent, J.W., Mansinghka, V.: A new approach to probabilistic programming inference. In: Proceedings of the 17th International conference on Artificial Intelligence and Statistics, pp. 1024–1032 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Narayanan, P., Carette, J., Romano, W., Shan, Cc., Zinkov, R. (2016). Probabilistic Inference by Program Transformation in Hakaru (System Description). In: Kiselyov, O., King, A. (eds) Functional and Logic Programming. FLOPS 2016. Lecture Notes in Computer Science(), vol 9613. Springer, Cham. https://doi.org/10.1007/978-3-319-29604-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-29604-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29603-6
Online ISBN: 978-3-319-29604-3
eBook Packages: Computer ScienceComputer Science (R0)