Probabilistic Software

Extracted Attributes

• Goal

  Unify probabilistic modeling and traditional general-purpose programming

• Field

  Computer Science, Artificial Intelligence

• Purpose

  Create systems that help make decisions in the face of uncertainty

• Paradigm

  Probabilistic Programming

• Key Concepts

  Bayesian networks, Markov models, Monte Carlo methods, probabilistic programming languages

• Core Functionality

  Declarative specification of probabilistic models with automatic inference

• Maturity (Probabilistic Genotyping Software)

  Available by late 1990s, not fully mature

Timeline

• Probabilistic Genotyping Software (PGS) became available, marking an early practical application of probabilistic software. (Source: web_search_results)

  1990s (late)

Probabilistic programming

Probabilistic programming (PP) is a programming paradigm based on the declarative specification of probabilistic models, for which inference is performed automatically. Probabilistic programming attempts to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable. It can be used to create systems that help make decisions in the face of uncertainty. Programming languages following the probabilistic programming paradigm are referred to as "probabilistic programming languages" (PPLs).

Web Search Results

• Probabilistic Genotyping Software

  PROBABILISTIC GENOTYPING SOFTWARE /// THE TECHNOLOGY What is it? Probabilistic genotyping software (PGS) is used in criminal investigations to help link a genetic sample — such as a sample from crime-scene evidence — to a person of interest (POI). It facilitates genetic analysis in complicated situations, such as when a sample is partially degraded or contains DNA from more than one person. [...] Butler JM, Kline MC, Coble MD “NIST interlaboratory studies involving DNA mixtures (MIX05 and MIX13): Variation observed and lessons learned.” FSIG 37:81-94 (2018). Coble MD, Bright J “Probabilistic genotyping software: An overview.” Forensic Science International: Genetics 38:219-224 (2019). President’s Council of Advisors on Science and Technology “Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods.” (Washington, D.C., September 2016). [...] How mature is it? PGS was available by the late 1990s, yet it is not fully mature. There are several software packages for PGS, some open source, some commercial. About 100 laboratories in the United States reportedly use PGS. PGS analyses are used by law enforcement offices, crime or forensics laboratories, defense attorneys, and law offices at the county, city, state, and federal levels. For example, according to a President’s Council of Advisors on Science and Technology (PCAST), the FBI

• Probabilistic programming

  Probabilistic programming (PP) is a programming paradigm based on the declarative specification of probabilistic models, for which inference is performed automatically. Probabilistic programming attempts to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable. It can be used to create systems that help make decisions in the face of uncertainty. Programming languages following the probabilistic programming paradigm [...] Jump to content # Probabilistic programming Azərbaycanca Català Español Français Српски / srpski 中文 Edit links From Wikipedia, the free encyclopedia Software system for statistical models [...] Main article: Probabilistic logic programming Probabilistic logic programming is a programming paradigm that extends logic programming with probabilities. Most approaches to probabilistic logic programming are based on the distribution semantics, which splits a program into a set of probabilistic facts and a logic program. It defines a probability distribution on interpretations of the Herbrand universe of the program. ### List of probabilistic programming languages [edit]

• Introduction of Probabilistic Computing

  Improve Suggest changes Like Article Like Report Probabilistic computing is a field of computer science and artificial intelligence that focuses on the study and implementation of probabilistic algorithms, models, and methods for computation. It aims to build systems that can reason about and handle uncertainty, making probabilistic predictions about the world and making decisions based on those predictions. [...] 2. Some of the key concepts in probabilistic computing include Bayesian networks, Markov models, Monte Carlo methods, and probabilistic programming languages. These tools and techniques allow computers to perform tasks such as uncertainty quantification, probabilistic inference, and decision-making under uncertainty. [...] 3. Probabilistic computing has a wide range of applications, including machine learning, robotics, computer vision, natural language processing, and cognitive computing. In recent years, the field has seen significant advances, driven by the increasing availability of large amounts of data and the development of powerful computational tools.

• Probabilistic Programming

  The first thing that makes a language a probabilistic programming language (PPL) is a set of primitives for drawing random numbers. At this point, a PPL looks like any old imperative language with a `rand` call. Here's an incredibly boring webppl program: `rand` `var b = flip(0.5); b ? "yes" : "no"` [...] ,iVBORw0KGgoAAAANSUhEUgAAAZEAAAArCAYAAAC5B7XwAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAMQUlEQVR42u1d63HbOBD+NnMFME4FoTuQkwqO7kBOKojcgTSpICN1QLuCmO5ATgeROhCvgljsAPeDixOMA0nwIVEi95vBXM4iCOK1byxIKYVLBhEFABLHT4lS6gGCc5+/KYCtUiqV0Rj8XMteHSD+Gkg/IsffhChdBhIAKwALGYpRQPbqwPBuQH1ZKaXIKPcyvRchmQJAIKMxfCilMnOPAngvozIAJkJEMRGplmVNRHMiCsdC/IhoR0SxLCGBQNAxfZkyTd0R0V7TGiKanKsmsgRwC+CuwKSw5d9ujXIH4J6ffwDwid+jOzt0yfIRQMj9Foxncyc1hau5jJqgxvoKiWjH9GUN4EYp9Z5p7hWATR+CKxFNmKkFRSrmmwJgDkAZJbKfcRVHvalPvbaFTSEKwPJE7UVGH3enaHOoxZi [...] ,iVBORw0KGgoAAAANSUhEUgAAAZEAAAArCAYAAAC5B7XwAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAMQUlEQVR42u1d63HbOBD+NnMFME4FoTuQkwqO7kBOKojcgTSpICN1QLuCmO5ATgeROhCvgljsAPeDixOMA0nwIVEi95vBXM4iCOK1byxIKYVLBhEFABLHT4lS6gGCc5+/KYCtUiqV0Rj8XMteHSD+Gkg/IsffhChdBhIAKwALGYpRQPbqwPBuQH1ZKaXIKPcyvRchmQJAIKMxfCilMnOPAngvozIAJkJEMRGplmVNRHMiCsdC/IhoR0SxLCGBQNAxfZkyTd0R0V7TGiKanKsmsgRwC+CuwKSw5d9ujXIH4J6ffwDwid+jOzt0yfIRQMj9Foxncyc1hau5jJqgxvoKiWjH9GUN4EYp9Z5p7hWATR+CKxFNmKkFRSrmmwJgDkAZJbKfcRVHvalPvbaFTSEKwPJE7UVGH3enaHOoxZi

• Probability in Computer Science

  Computer Vision: Probabilistic methods are applied to interpret and process visual data from images or videos. For example: Object Recognition: Algorithms use probabilistic models to recognize objects in images based on training data. Image Segmentation: Models like conditional random fields (CRFs) probabilistically divide images into segments based on pixel values. [...] Recommendation Systems: Platforms like Netflix and Amazon use probabilistic models (e.g., collaborative filtering) to predict user preferences and recommend products or content based on past behavior. Finance and Economics: Probability models help in assessing risks and returns in financial markets, guiding investment strategies and economic forecasting. [...] Machine Learning: Many Machine Learning algorithms rely on probabilistic reasoning to make predictions and classifications based on data. Some common applications include Naive Bayes Classifier (Uses Conditional Probability), Hidden Markov Models(Used in speech recognition natural language processing, and time-series data analysis) and Probabilistic Graphical Models(Bayesian networks and Markov random fields, used for structured prediction problems)