The document discusses the advantages of probabilistic programming, specifically through the Figaro library, which simplifies the creation of applications for predicting, inferring, and learning from data. It highlights Figaro's general-purpose inferencing capabilities, ease of integration, and suitability for domain experts with limited machine learning knowledge. The goal is to automate inference methods to enhance the usability of statistical models in diverse applications, while still acknowledging the need for some expertise in model representation and algorithm configuration.

1. Practical Probabilistic Programming with Figaro
Avi Pfeffer
Charles River Analytics
MLConf May 20, 2016

2.  Why Probabilistic Programming?
 Figaro
 Examples and Applications
 Where We’re Going
Overview

3.  We want to
 Predict the future
 Infer past causes of current observations
 Learn from experience
 With much less effort and expertise than before
What Are We Trying To Do?

4. Probabilistic Reasoning Lets You Do All These Things

5. Probabilistic Reasoning: Predicting the Future

6. Probabilistic Reasoning: Inferring Factors that Caused
Observations

7. Probabilistic Reasoning: Using the Past to Predict the
Future

8. Probabilistic Reasoning: Learning from the Past

9.  You need to
 Implement the representation
 Implement the probabilistic inference algorithm
 Implement the learning algorithm
 Interact with data
 Integrate with an application
But Probabilistic Reasoning Is Hard!

10. Drastically reduce the work to create
probabilistic reasoning applications
Goal of Probabilistic Programming

11. 1. Expressive programming language for representing models
2. General-purpose inference and learning algorithms apply to
models written in the language
All you have to do is represent the model in code and you
automatically get the application
How Probabilistic Programming Achieves This

12.  It’s easy to incorporate rich domain knowledge into
probabilistic programs
 Probabilistic programming can work well even when you don’t
have a lot of data
 Probabilistic programming models are explainable and
understandable
 Probabilistic programming can predict outputs belonging to
complex data types of variable size, like social networks
Probabilistic Programming Compared to Deep Learning

13.  Why Probabilistic Programming?
 Figaro
 Examples and Applications
 Where We’re Going
Overview

14. Figaro goals
A probabilistic programming system that is:
 Easy to interact with data
 Easy to integrate with applications
 General and expressive representation to capture common
programming patterns
 An extensible library of inference algorithms

15.  Figaro provides data structures to represent probabilistic
programs
 Scala programs construct the Figaro models
 Inference algorithms implemented in Scala operate on these
models
Figaro as a Scala Library

16.  Easy interaction with data and integration with applications
 Can embed general-purpose code in probabilistic programs
 Can construct models programmatically
 Figaro inherits functional and object-oriented features of Scala
 Can use Scala functions to specify constraints
 Scala supports extensible library of inference algorithms
Advantages of Scala Embedding

17.  Hard to reason about models at source level, since arbitrary
Scala code may be embedded in model
 Syntax not as elegant as self-contained languages
 Steeper learning curve
 You need to learn Scala and Figaro
 But we have found that beginners can easily learn to write models
quickly
We have found that the power and practicality of Figaro more
than make up for these disadvantages
Disadvantages of Scala Embedding

18.  Why Probabilistic Programming?
 Figaro
 Examples and Applications
 Where We’re Going
Overview

19. Figaro novices were able to quickly build up an
integrated probabilistic reasoning application
Hydrological Terrain Modeling for Army Logistics

20. We were able to perform a sophisticated analysis far
better than our previous non-probabilistic method
Malware Lineage (DARPA Cyber Genome)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Parent
Correct
Parent
Precision
Parent
Recall
Parent
FMeasure
New Algorithm
Old Algorithm
With New
Features
Phase I IV &V
Result

21. Tracklet Merging (DARPA PPAML Challenge Problem)
0.3
0.2
0.5
0.3
0.2
0.5
0.7
0.2
0.1
0.7
0.2
0.1
=
We came up with a new algorithm that
we would not have thought of without
probabilistic programming and
expressed it in one slide

22. class Tracklet(
toCandidates: List[(Double, Tracklet)],
fromCandidates: List[(Double, Tracklet)]
){
val next = Select(toCandidates: _*)
val previous = Select(fromCandidates: _*)
}
for (source <- sources) {
val nextPrevious =
Chain(source.next,
nextTracklet => nextTracklet.previous)
nextPrevious.observe(source)
}
Tracklet Merging in Figaro

23.  Why Probabilistic Programming?
 Figaro
 Examples and Applications
 Where We’re Going
Overview

24.  We’ve significantly reduced the effort required to build complex
probabilistic reasoning applications
 But it still requires a fair amount of machine learning expertise
to make these applications work
 You need to know how to represent models
 You need to know how to choose and configure inference algorithms
Current State of the Art

25. A probabilistic programming framework that domain experts with
little or no machine learning knowledge can use
1. An English-like language for describing a domain
2. A method for automatically filling in the gaps in a model
3. Automated inference techniques that optimally choose and
configure algorithms for a particular problem
Our Goal

26. 1. Decompose an inference problem into many subproblems
2. Optimize the choice an appropriate solver for each
subproblem
3. Combine the subproblem solutions into a solution of the
whole problem
Automated Inference Strategy

27.  Subproblems are represented as factor graphs
 Factored algorithms are used to solve subproblems
 E.g., variable elimination, belief propagation, Gibbs sampling
 We intelligently choose between the available algorithms on
each subproblem
Structured Factored Inference (SFI)

28. Compiled Graphical Model of Figaro Program
aMUX
fb
T fb
F
MUX
fc
T fc
F
b c
x1b
T x2b
T
y1b
T y2b
T
x1b
F x2b
F
y1b
F y2b
F
x1c
T x2c
T
y1c
T y2c
T
x1c
F x2c
F
y1c
F y2c
F

29. Decompose Problem Automatically
aMUX
fb
T fb
F
MUX
fc
T fc
F
b c
x1b
T x2b
T
y1b
T y2b
T
x1b
F x2b
F
y1b
F y2b
F
x1c
T x2c
T
y1c
T y2c
T
x1c
F x2c
F
y1c
F y2c
F
Subproblems
Top level problem

30. Combine and Reuse Solutions
aMUX
fb
T fb
F
MUX
fc
T fc
F
b c
x1b
T x2b
T
y1b
T y2b
T
x1b
F x2b
F
y1b
F y2b
F
x1c
T x2c
T
y1c
T y2c
T
x1c
F x2c
F
y1c
F y2c
F
Subproblems
Top level problem
pT pF pT pF

31. Optimize Each Subproblem Individually
Results on a model structure used for medical diagnosis
Number of diseases
L1Error

32.  It’s easy to write probabilistic programs that define very large or
even infinite factor graphs
32
Challenge
You can’t construct the factor graph

33.  We can solve problems with infinitely many variables
 Partially expand the problem
 Quantify the effect of the unexpanded part of the program on the
query
 Produces lower and upper bounds on answer to the query
 As you expand more of the problem, the bounds get tighter
Lazy Inference

34. Grammar with Sentences of Unbounded Length
34

35. Grammar with Infinite Sentences
35

36.  Probabilistic reasoning helps you predict, infer, and learn
 Probabilistic programming makes this much easier!
 Figaro is a mature, practical probabilistic programming system
with many applications
 We’re striving to make probabilistic programming even easier!
Conclusion

37.  This material is based upon work supported by the United
States Air Force under Contract No. FA8750-14-C-0011.
 Any opinions, findings and conclusions or recommendations
expressed in this material are those of the author(s) and do not
necessarily reflect the views of the United States Air Force.
Acknowledgement

38. More Information
• Figaro is open source
 Contributions welcome!
 Releases can be downloaded from
www.cra.com/figaro
 Figaro source is on GitHub at
www.github.com/p2t2
 Version 4.0 was released in March
• If you have any questions, feel
free to contact me at
apfeffer@cra.com
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