Jan 14, 2019 · Modern probabilistic programming tools can automatically generate an ML algorithm from the model you specified, using a general-purpose inference method. You don’t even need to know much about it, because it’s already implemented for you.

Instead, probabilistic programming is a tool for statistical modeling. The idea is to borrow lessons from the world of programming languages and apply them to the problems of designing and using statistical models.

May 29, 2023 · Probabilistic models are an essential component of machine learning, which aims to learn patterns from data and make predictions on new, unseen data. They are statistical models that capture the inherent uncertainty in data and incorporate it into their predictions.

Using a common architecture for probabilistic programming applications · Designing a realistic model using only simple language features · Learning models from data and using the results to reason about future instances

ProbLog is a probabilistic programming language that extends Prolog along the lines of Sato's distribution semantics. Its development focusses especially on machine learning techniques and implementation aspects.

Jun 2, 2023 · In this article, we’ll take a comprehensive look at the concept of probabilistic learning, explore its key algorithms, and then illustrate its implementation through a hands-on Python example...

Sep 27, 2018 · We start with a discussion of model-based reasoning and explain why conditioning is a foundational computation central to the fields of probabilistic machine learning and artificial intelligence.

In this chapter, you’ll learn how to make everyday decisions by using a probabilistic model and an inference algorithm—the two main components of a probabilistic reasoning system.

In this tutorial, we provide an in-depth tutorial on Scenic, a simulator-agnostic probabilistic programming language to model complex multi-agent, physical environments with stochasticity and spatio-temporal constraints.

Introduction to Probabilistic Machine Learning. Things they don't tell us... The purpose of this booklet is to give the foundations and intuitions for probablistic machine learning. The targeted audience are Computer Sci-entists who might have missed out on some critical components in their mathematical education.