# probabilistic programming

> programming paradigm designed to describe probabilistic models and then perform inference in those models

**Wikidata**: [Q7246865](https://www.wikidata.org/wiki/Q7246865)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Probabilistic_programming)  
**Source**: https://4ort.xyz/entity/probabilistic-programming

## Summary
Probabilistic programming is a programming paradigm specifically designed to describe probabilistic models and perform inference within them. It unites concepts from programming languages and statistics to facilitate the creation of systems that can reason about uncertainty, serving as a key facet of machine learning and inductive programming.

## Key Facts
- **Classification:** Probabilistic programming is an instance of a **programming paradigm** and a **type of programming language**.
- **Core Function:** Its primary design is to **describe probabilistic models** and then **perform inference** in those models.
- **Primary Application:** The paradigm is specifically used for **Bayesian inference**.
- **Domain:** It is a **facet of machine learning** and has a **field of work** in **statistics**.
- **Also Known As:** PPL (Probabilistic Programming Language), probabilistic relational programming language.
- **Structural Lineage:** It is a **subclass of programming language** and a **manifestation of inductive programming**.
- **Related Technologies:** **Stan** is a notable related entity, identified as a probabilistic programming language for Bayesian inference.

## FAQs
### Q: What is the primary purpose of probabilistic programming?
A: The primary purpose is to describe probabilistic models and perform inference on them. It allows programmers to specify complex statistical models using code rather than pure mathematical notation.

### Q: How does probabilistic programming relate to machine learning?
A: Probabilistic programming is considered a facet of machine learning. It provides the linguistic tools to build models that learn from data by performing inference.

### Q: Is probabilistic programming related to Bayesian statistics?
A: Yes, the paradigm is explicitly used for Bayesian inference. It enables the practical application of Bayesian methods by automating the inference process within defined models.

### Q: What kind of programming paradigm is it?
A: It is classified as a programming paradigm and a manifestation of inductive programming. This means it focuses on deriving rules or patterns from data (inference) rather than just following explicit instructions.

## Why It Matters
Probabilistic programming represents a significant intersection between computer science and statistics. By treating probability as a core building block of a programming language, it allows developers to create models that inherently account for uncertainty. This is a departure from traditional deterministic programming, where variables hold single fixed values rather than probability distributions.

The paradigm matters because it democratizes access to complex machine learning techniques. Before probabilistic programming, applying Bayesian inference often required deep mathematical expertise to derive custom inference algorithms for every new model. Probabilistic programming decouples model description from inference execution; the user writes the model, and the language compiler handles the complex mathematical inference. This abstraction accelerates development in fields like robotics, artificial intelligence, and data analysis, allowing for rapid prototyping and deployment of models that can reason under uncertainty.

## Notable For
- **Unifying Logic and Probability:** It combines the syntax of programming languages with the mathematical rigor of probability theory.
- **Automated Inference:** It is distinguished by its ability to automatically run inference procedures on models defined by the user.
- **Inductive Capabilities:** It is a manifestation of inductive programming, focusing on deriving general rules from specific observations.
- **Specialized Dialects:** It has spawned specific languages (like Stan) dedicated solely to statistical computation and Bayesian inference.

## Body
### Definition and Classification
Probabilistic programming is defined technically as a programming paradigm. While it falls under the broad umbrella of programming languages, its specific utility is the description of probabilistic models. It is structurally classified as a subclass of **programming language** and serves as a manifestation of **inductive programming**.

### Core Functionality
The operational goal of a probabilistic programming system is two-fold:
1.  **Model Description:** Allowing the user to define a probabilistic model using programming logic.
2.  **Inference:** Performing the mathematical computation necessary to draw conclusions from that model.

This process is most commonly applied to **Bayesian inference**, allowing for the updating of probabilities as more evidence becomes available.

### Domain and Context
The paradigm operates at the intersection of two major fields:
-   **Machine Learning:** It is considered a facet of machine learning, providing tools for creating predictive models.
-   **Statistics:** It is a field of work within statistics, offering new ways to apply statistical methods computationally.

### Related Entities
A prominent example of this paradigm in action is **Stan**. Stan is explicitly categorized as a probabilistic programming language designed for high-performance statistical computation, specifically Bayesian inference.