From Practical Probabilistic Programming by Avi Pfeffer
This article discusses forward sampling.
By Avi Pfeffer
A spam filter consists of two components. In this article, based on my book Practical Probabilistic Programming , first describe the architecture of the reasoning component and then the learning component architecture.
By Avi Pfeffer , author of Practical Probabilistic Programming
In this article, I’ll talk about Bayesian networks, which are the standard framework for encoding asymmetric relationships using directed dependencies.
Bayesian Networks (PDF)
In this article, excerpted from Practical Probabilistic Programming by Avi Pfeffer, we’ll build the simplest Figaro model possible.
Your First Model (PDF)
By Avi Pfeffer
An open universe situation is where you don’t know how many objects there are. This article, excerpted from Practical Probabilistic Programming, focuses on number uncertainty, which is addressed by variable size arrays.
Open universe situations with unknown number of objects (PDF)
By Avi Pfeffer, author of Practical Probabilistic Programming
Probabilistic programming is a way to create systems that help us make decisions in the face of uncertainty. Probabilistic reasoning combines our knowledge of a situation with the laws of probability to determine those unobserved factors that are critical to the decision. Until recently, probabilistic reasoning systems have been limited in scope, and have been hard to apply to many real world situations. Probabilistic programming is a new approach that makes probabilistic reasoning systems easier to build and more widely applicable.
