This tutorial assumes that you have some form of Anaconda Python (with Python version 3.11) setup and installed on your system. If you do not, please download and ...

Probabilistic programming has emerged as a powerful paradigm for constructing and analysing statistical models by combining the expressiveness of modern programming languages with the rigour of ...

Probabilistic programming is an approach to computing based on the idea that probabilistic models can be naturally and efficiently represented as executable code. This idea has enabled researchers to ...

For humans and machines, intelligence requires making sense of the world — inferring simple explanations for the mishmosh of information coming in through our senses, discovering regularities and ...

Probabilistic Programming is a way of defining probabilistic models by overloading the operations in standard programming language to have probabilistic meanings. The goal is to specify probabilistic ...

Generative models of tabular data are key in Bayesian analysis, probabilistic machine learning, and fields like econometrics, healthcare, and systems biology. Researchers have developed methods to ...

The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

This repository contains my implementation of the Probabilistic Roadmap Method (PRM), a sampling-based path planner developed as a programming project for the Coursera course: "Modern Robotics, Course ...