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Probabilistic Programming Systems aim to merge general purpose programming with probabilistic modelling. They provide powerful statistical inference and thus allow developers to focus on the modelling with tools and environments they are comfortable with. These emerging methods are promising additions to the Data Scientist’s toolbox and an interesting, satisfying playground for programming enthusiasts. This talk is an introduction to Probabilistic Programming Systems, their use and value for the industry and Clojure’s great library “Anglican”.

Software Developer and Complexity Tamer

- 1. Intro to Probabilistic Programming and Clojure’s Anglican Dr. Nils Blum-Oeste EuroClojure 2017, Berlin
- 2. Who is talking? Probabilistic Programming: Why? What? How? Anglican: Examples
- 3. Physics Chemistry Quantitative Modelling Complex Systems
- 4. 😍
- 5. Smart Home Energy Model
- 6. Probabilistic Programming Why? What? How?
- 7. Predict the future Infer causes of observations Improve models from evidence Why?
- 8. Compared to other machine learning techniques incorporates domain knowledge models are explainable and understandable works well with small to medium data need to build a model computationally expensive (aka “slow”) + -
- 9. Don’t think of Probabilistic Programming vs other techniques, they can complement each other
- 10. What is a Probabilistic Programming System?
- 11. Bayesian Probability: The belief or conﬁdence in an event occurring
- 12. Bayes Rule Marginal Likelihood PriorLikelihood Posterior Hypothesis Evidence
- 13. Markov chain Monte Carlo Sampling
- 14. sample at n ﬁxed positions too expensive for multiple dimensions
- 15. random sample next sample random step
- 16. sample density reﬂects probabilities works for many dimensions
- 17. Probabilistic Reasoning System Probabilistic Model Inference Algorithm Evidence Queries Answer
- 18. Probabilistic Programming System Probabilistic Model Inference Algorithm Evidence Queries Answer code provided by PPS data code data
- 19. Execution model probabilistic programs are executed many times for inference random choices affect the execution path on each run computes the distribution from which execution results are drawn
- 20. Anglican Examples
- 21. What is Anglican? embedded language, subset of Clojure same syntax but different semantics Clojure, JVM and Anglican available to each other Anglican runs wherever you have a JVM
- 22. A probabilistic Hello World: Coin Toss
- 23. Sampling a fair coin with Anglican
- 24. Learning the bias of a coin
- 25. Posterior distribution after observing 20 heads and 20 tails
- 26. Example: AB-Test
- 27. Simulate user interactions
- 28. Probabilistic model
- 29. Posterior sampling
- 30. Example: Bayesian Inference on Game Physics
- 31. The whole Clojure world available… No guarantee for success though!
- 32. PPS beyond Anglican Clojure, high performance: Bayadera Industry Standards: Stan, PyMC Many PPS: Figaro, WebPPL, BayesDB, …
- 33. Probabilistic Programming, my conclusions so far: Probabilistic Programming Systems: Very low entry barrier to predict, infer and train domain models, but modelling requires skill Anglican: Solid solution in Clojure space with great modelling and prototyping capabilities, but inference is slow and ecosystem is lacking Research Topic: Expect maturation and extension of the probabilistic programming techniques
- 34. Acknowledgements Anglican Devs: Frank Wood, David Tolpin & all contributors Speaker Mentor: Jan Stępień Discussions: Dragan Djuric, Chris Wallace, David Tolpin, Christian Weilbach
- 35. More Information http://www.robots.ox.ac.uk/~fwood/anglican/index.html http://probabilistic-programming.org

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