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Intro to Probabilistic Programming
and Clojure’s Anglican
Dr. Nils Blum-Oeste

EuroClojure 2017, Berlin
Who is talking?
Probabilistic Programming:
Why? What? How?
Anglican: Examples
Physics
Chemistry
Quantitative Modelling
Complex Systems
😍
Smart Home Energy Model
Probabilistic Programming
Why? What? How?
Predict the future
Infer causes of observations
Improve models from evidence
Why?
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”)
+ -
Don’t think of Probabilistic
Programming vs other techniques,
they can complement each other
What is a Probabilistic
Programming System?
Bayesian Probability: The belief or
confidence in an event occurring
Bayes Rule
Marginal Likelihood
PriorLikelihood
Posterior
Hypothesis Evidence
Markov chain Monte Carlo
Sampling
sample at n fixed positions
too expensive for multiple
dimensions
random sample
next sample
random step
sample density reflects
probabilities
works for many dimensions
Probabilistic Reasoning System
Probabilistic Model
Inference Algorithm
Evidence
Queries
Answer
Probabilistic Programming System
Probabilistic Model
Inference Algorithm
Evidence
Queries
Answer
code
provided by PPS
data
code
data
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
Anglican Examples
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
A probabilistic Hello World:
Coin Toss
Sampling a fair coin with Anglican
Learning the bias of a coin
Posterior distribution after observing 20 heads and 20 tails
Example: AB-Test
Simulate user interactions
Probabilistic model
Posterior sampling
Example: Bayesian Inference
on Game Physics
The whole Clojure world available…
No guarantee for success though!
PPS beyond Anglican
Clojure, high performance:
Bayadera
Industry Standards: Stan, PyMC
Many PPS: Figaro, WebPPL,
BayesDB, …
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
Acknowledgements
Anglican Devs: Frank Wood, David Tolpin & all contributors

Speaker Mentor: Jan Stępień

Discussions: Dragan Djuric, Chris Wallace, David Tolpin, Christian Weilbach
More Information
http://www.robots.ox.ac.uk/~fwood/anglican/index.html

http://probabilistic-programming.org

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Intro to Probabilistic Programming and Clojure’s Anglican