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aGrUM 3.2.0
a C++ library for (probabilistic) graphical models
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aGrUM 3.2.0
a C++ library for (probabilistic) graphical models
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| Technical topics | |
| Build System | AGrUM/pyAgrum are built with CMake, driven through a thin Python wrapper called act (acttools/) |
| Code modularization (aGrUM and pyAgrum) | |
| Basic data structures | |
| Hash Tables | A efficient and flexible implementation of hash tables |
| Hash functions | This module lists all hash functions provided by aGrUM |
| Bijections | Set of pairs of elements with fast search for both elements |
| Heaps | An implementation of heaps |
| Lists | This file provides class List for manipulating generic lists as well as List<>::iterator, List<>::const_iterator, List<>::iterator_safe and List<>::const_iterator_safe for parsing lists |
| Priority queue | This file provides class MultiPriorityQueue that is essentially a heap in which elements are sorted according to a dynamically modifiable priority |
| Sequences | A Sequence<Key> is quite similar to a vector<Key> in that it stores an ordered set of elements |
| Sets | A Set is a structure that contains arbitrary elements |
| Splay Trees | A splay tree is a self-balancing binary search tree |
| Graph representation | |
| Generic algorithms | Generic algorithms come in two flavors: approximation schemes/policies (see the Topics below) and a family of graph algorithms genericized via C++20 concepts, detailed in the section below |
| Approximation Scheme algorithms | About aGrUM approximation schemes |
| Approximation algorithms | About aGrUM approximation policies |
| Signaler and Listener | |
| Utilities | About aGrUM utilities |
| Math | All the maths you'll need |
| Configuration | About aGrUM configuration |
| Smart Pointers | RefPtr are a replacement for the usual pointers: they keep track of the number of "smart" pointers pointing to a given element |
| Theoretical topics | |
| multidimensional tables | |
| Operators on multidimensional tables | |
| Aggregators | |
| Function Graphs | |
| Patterns for multidimensional tables | |
| Bayesian networks | |
| Inference Algorithms for Bayesian networks | |
| Serialization of Bayesian networks | |
| Generators | |
| Particles Algorithms | |
| Tools for learning | |
| Database Manipulations | |
| Scores and Independence Tests | |
| Scores A Priori | |
| Structural Constraints | |
| Parameters utilities | |
| Causal Model | Causal graphs and causal inference on top of Bayesian networks: do-calculus criteria (front-door/back-door), causal impact estimation and counterfactual reasoning (module CM, depends on BN) |
| k-order Dynamic Bayesian Networks | |
| Generators | |
| Inference Algorithms for k-TBN | |
| Learning Algorithms for k-TBN | |
| Database Generation for k-TBN | |
| Markov random fields | |
| Inference Algorithms for Markov random fields | |
| Serialization of Markov random fields | |
| Influence Diagram | Influence diagrams extend Bayesian networks with decision nodes and utility nodes, for decision-theoretic planning under uncertainty (module ID, depends on BN) |
| Credal Networks | |
| Factored Markov Decision Process | |
| Probabilistic Relational Models |