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- Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
- AISTATS
- 2014

We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to implement and easy toâ€¦ (More)

- Francis P. Kuhajda, K. curt s. Jenner, +4 authors G. Pasternack
- Proceedings of the National Academy of Sciencesâ€¦
- 1994

OA-519 is a prognostic molecule found in tumor cells from breast cancer patients with markedly worsened prognosis. We purified OA-519 from human breast carcinoma cells, obtained its peptide sequence,â€¦ (More)

- Frank D. Wood, Thomas L. Griffiths
- NIPS
- 2006

Many unsupervised learning problems can be expressed as a fo rm o matrix factorization, reconstructing an observed data matrix as the p roduct of two matrices of latent variables. A standardâ€¦ (More)

We introduce hierarchically supervised latent Dirichlet allocation (HSLDA), a model for hierarchically and multiply labeled bag-of-word data. Examples of such data include web pages and theirâ€¦ (More)

We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithmsâ€¦ (More)

- Jan Gasthaus, Frank D. Wood, Yee Whye Teh
- 2010 Data Compression Conference
- 2010

In this work we describe a sequence compression method based on combining a Bayesian nonparametric sequence model with entropy encoding. The model, a hierarchy of Pitman-Yor processes of unboundedâ€¦ (More)

- David Tolpin, Brooks Paige, Frank D. Wood
- ArXiv
- 2015

We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to the models. Thisâ€¦ (More)

We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares statistical strengthâ€¦ (More)

- Frank D. Wood, Jan Gasthaus, CÃ©dric Archambeau, Lancelot James, Yee Whye Teh
- Commun. ACM
- 2011

Probabilistic models of sequences play a central role in most machine translation, automated speech recognition, lossless compression, spell-checking, and gene identification applications to name butâ€¦ (More)

BACKGROUND AND OBJECTIVE
The volume of healthcare data is growing rapidly with the adoption of health information technology. We focus on automated ICD9 code assignment from discharge summary contentâ€¦ (More)