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pgmpy: Python Library for Probabilistic Graphical Models
pgmpy is an open-source Python library for working with Probabilistic Graphical Models, specifically focusing on Bayesian Networks. It provides functionalities for structure learning, parameter learning, inference, and causality.
Python
Added on 2025年5月26日2,926
Stars791
ForksPython
LanguageProject Introduction
Summary
pgmpy is a comprehensive library built in Python that provides tools for creating, analyzing, and working with various types of Probabilistic Graphical Models (PGMs), with a strong emphasis on Bayesian Networks.
Problem Solved
Analyzing complex systems with uncertain relationships and causal dependencies often requires powerful probabilistic modeling techniques. pgmpy addresses this by offering a robust framework for building, learning, and performing inference on graphical models.
Core Features
Structure Learning
Algorithms to learn the graphical structure of a Bayesian Network from data.
Parameter Learning
Methods to learn the conditional probability distributions or parameters for a given network structure.
Inference Algorithms
Supports various inference algorithms like Variable Elimination and Belief Propagation to answer probabilistic queries on the network.
Tech Stack
Python
NumPy
SciPy
NetworkX
Pandas
使用场景
pgmpy is applicable in numerous domains where understanding relationships, uncertainty, and causality from data is crucial, including:
Medical Diagnosis
Details
Model dependencies between symptoms, diseases, and test results to aid in diagnosis and understand probabilistic outcomes.
User Value
Improve accuracy and interpretability of diagnostic models; quantify uncertainty.
System Fault Diagnosis
Details
Build models to identify the root cause of failures in complex systems based on observed symptoms or sensor readings.
User Value
Reduce downtime and maintenance costs by quickly identifying failure points.
Risk Assessment
Details
Model interconnected risks and dependencies in finance, insurance, or project management to quantify overall risk exposure.
User Value
Enable better decision-making under uncertainty; understand the impact of different factors on risk.
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