# Lectures

To get up-to-date with the recent research in probabilistic programming you can take a look at Hongseok Yang’s lecture.

## Papers

If you use Anglican for research, please cite the Anglican papers below.

#### Current Anglican implementation (please cite this as an Anglican user)

1. Tolpin, D., van de Meent, J. W., Yang, H., & Wood, F. (2016). Design and Implementation of Probabilistic Programming Language Anglican. ArXiv Preprint ArXiv:1608.05263. BIB
@article{tolpin2016design,
title = {Design and Implementation of Probabilistic Programming Language Anglican},
author = {Tolpin, David and van de Meent, Jan Willem and Yang, Hongseok and Wood, Frank},
journal = {arXiv preprint arXiv:1608.05263},
year = {2016}
}


#### Original AISTATS (please cite this if referring in general to the Anglican system but read the arXiv version below for modern syntax)

1. Wood, F., van de Meent, J. W., & Mansinghka, V. (2014). A New Approach to Probabilistic Programming Inference. In Proceedings of the 17th International conference on Artificial Intelligence and Statistics (pp. 1024–1032). BIB PDF
@inproceedings{wood-aistats-2014,
author = {Wood, Frank and van de Meent, Jan Willem and Mansinghka, Vikash},
booktitle = {Proceedings of the 17th International conference on Artificial Intelligence and Statistics},
title = {A New Approach to Probabilistic Programming Inference},
pages = {1024-1032},
year = {2014}
}


#### arXiv version, updated to new Clojure-based language syntax

1. Wood, F., van de Meent, J. W., & Mansinghka, V. (2015). A New Approach to Probabilistic Programming Inference. ArXiv e-Prints. BIB PDF
@article{wood-aistats-2014b,
author = {{Wood}, Frank and {van de Meent}, Jan Willem and {Mansinghka}, Vikash},
title = {{A New Approach to Probabilistic Programming Inference}},
journal = {ArXiv e-prints},
archiveprefix = {arXiv},
eprint = {1507.00996},
primaryclass = {stat.ML},
keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Programming Languages},
year = {2015},
month = jul
}


## Optimization Algorithms

1. Rainforth, T., Le, T. A., van de Meent, J.-W., Osborne, M. A., & Wood, F. (2016). Bayesian Optimization for Probabilistic Programs. In Advances in Neural Information Processing Systems (NIPS) (pp. 280–288). BIB
@inproceedings{rainforth-nips-2016,
title = {{B}ayesian {O}ptimization for {P}robabilistic {P}rograms},
author = {Rainforth, Tom and Le, Tuan Anh and van de Meent, Jan-Willem and Osborne, Michael A and Wood, Frank},
booktitle = {Advances in Neural Information Processing Systems (NIPS)},
year = {2016},
pages = {280--288}
}


## Inference Algorithms

#### Interacting Particle Markov Chain Monte Carlo (ipmcmc)

1. Rainforth, T., Naesseth, C. A., Lindsten, F., Paige, B., van de Meent, J. W., Doucet, A., & Wood, F. (2016). Interacting Particle Markov Chain Monte Carlo. In Proceedings of the 33rd International Conference on Machine Learning (Vol. 48). BIB
@inproceedings{rainforth2016interacting,
title = {Interacting Particle {M}arkov Chain {M}onte {C}arlo},
author = {Rainforth, T. and Naesseth, C.A. and Lindsten, F. and Paige, B. and van de Meent, J.W. and Doucet, A. and Wood, F.},
booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
series = {JMLR},
volume = {48},
year = {2016}
}


#### Particle Gibbs (pgibbs), Particle independent Metropolis-Hastings (pimh), Sequential Monte Carlo (smc)

1. Wood, F., van de Meent, J. W., & Mansinghka, V. (2014). A New Approach to Probabilistic Programming Inference. In Proceedings of the 17th International conference on Artificial Intelligence and Statistics (pp. 1024–1032). BIB PDF
@inproceedings{wood-aistats-2014,
author = {Wood, Frank and van de Meent, Jan Willem and Mansinghka, Vikash},
booktitle = {Proceedings of the 17th International conference on Artificial Intelligence and Statistics},
title = {A New Approach to Probabilistic Programming Inference},
pages = {1024-1032},
year = {2014}
}

1. Paige, B., & Wood, F. (2014). A Compilation Target for Probabilistic Programming Languages. In Proceedings of The 31st International Conference on Machine Learning (pp. 1935–1943). BIB PDF
@inproceedings{paige-icml-2014,
title = {A Compilation Target for Probabilistic Programming Languages},
author = {Paige, Brooks and Wood, Frank},
booktitle = {Proceedings of The 31st International Conference on Machine Learning},
pages = {1935–1943},
year = {2014}
}


#### Particle Cascade (pcascade)

1. Paige, B., Wood, F., Doucet, A., & Teh, Y. W. (2014). Asynchronous Anytime Sequential Monte Carlo. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (pp. 3410–3418). Curran Associates, Inc. BIB PDF
@incollection{paige-nips-2014,
title = {Asynchronous Anytime Sequential Monte Carlo},
author = {Paige, Brooks and Wood, Frank and Doucet, Arnaud and Teh, Yee Whye},
booktitle = {Advances in Neural Information Processing Systems 27},
editor = {Ghahramani, Z. and Welling, M. and Cortes, C. and Lawrence, N.D. and Weinberger, K.Q.},
pages = {3410--3418},
year = {2014},
publisher = {Curran Associates, Inc.}
}


#### Particle Gibbs with Ancestor Sampling (pgas)

1. van de Meent, J.-W., Yang, H., Mansinghka, V., & Wood, F. (2015). Particle Gibbs with Ancestor Sampling for Probabilistic Programs. In Proceedings of the 18th International conference on Artificial Intelligence and Statistics (pp. 986–994). ABS BIB PDF

Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on importance resampling, which results in degenerate particle trajectories and a low effective sample size for variables sampled early in a program. We here develop a formalism to adapt ancestor resampling, a technique that mitigates particle degeneracy, to the probabilistic programming setting. We present empirical results that demonstrate nontrivial performance gains.

@inproceedings{vandemeent-aistats-2015,
author = {van de Meent, Jan-Willem and Yang, Hongseok and Mansinghka, Vikash and Wood, Frank},
booktitle = {Proceedings of the 18th International conference on Artificial Intelligence and Statistics},
title = {{Particle Gibbs with Ancestor Sampling for Probabilistic Programs}},
pages = {986–994},
year = {2015}
}


#### Adaptive scheduling lightweight Metropolis-Hastings (almh)

1. Tolpin, D., van de Meent, J.-W., Paige, B., & Wood, F. (2015). Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. In A. Appice, P. P. Rodrigues, V. Santos Costa, J. Gama, A. Jorge, & C. Soares (Eds.), Machine Learning and Knowledge Discovery in Databases (Vol. 9285, pp. 311–326). Springer International Publishing. https://doi.org/10.1007/978-3-319-23525-7_19 BIB PDF
@incollection{tolpin-ecml-2015,
year = {2015},
isbn = {978-3-319-23524-0},
booktitle = {Machine Learning and Knowledge Discovery in Databases},
volume = {9285},
series = {Lecture Notes in Computer Science},
editor = {Appice, Annalisa and Rodrigues, Pedro Pereira and Santos Costa, Vítor and Gama, João and Jorge, Alípio and Soares, Carlos},
doi = {10.1007/978-3-319-23525-7_19},
title = {Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs},
url = {http://dx.doi.org/10.1007/978-3-319-23525-7_19},
publisher = {Springer International Publishing},
keywords = {Probabilistic programming; Adaptive MCMC},
author = {Tolpin, David and van de Meent, Jan-Willem and Paige, Brooks and Wood, Frank},
pages = {311-326},
language = {English}
}


#### Bayesian Ascent Monte Carlo (bamc)

1. Tolpin, D., & Wood, F. (2015). Maximum a Posteriori Estimation by Search in Probabilistic Programs. In Proceedings of the Eighth Annual Symposium on Combinatorial Search (pp. 201–205). BIB PDF
@inproceedings{tolpin-socs-2015,
author = {Tolpin, David and Wood, Frank},
booktitle = {Proceedings of the Eighth Annual Symposium on
Combinatorial Search},
title = {Maximum a Posteriori Estimation by Search in
Probabilistic Programs},
year = {2015},
pages = {201--205}
}