Welcome to OpenDEN
Custom Search
Artificial Intelligence
Artificial Intelligence :
The use of programs to enable machines to perform tasks which humans perform using their intelligence. Early AI avoided human pychological models, but this orientation has been altered by the development of connectionism, which is based on theories of how the brain works. In connectionism, complex functions, including learning, involve the transmission of information along pathways formed among large arrays of simple elements
From: www.filosofia.net/materiales/rec/glosaen.htm
BNT
BNT supports many types of conditional probability distributions (nodes), and it is easy to add more.
Tabular (multinomial)
Gaussian
Softmax (logistic/ sigmoid)
Multi-layer perceptron (neural network)
Noisy-or
Deterministic
BNT supports decision and utility nodes, as well as chance nodes, i.e., influence diagrams as well as Bayes nets.
BNT supports static and dynamic BNs (useful for modelling dynamical systems and sequence data).
BNT supports many different inference algorithms, and it is easy to add more.
BNT supports several methods for parameter learning, and it is easy to add more.
Batch MLE/MAP parameter learning using EM. (Each node type has its own M method, e.g. softmax nodes use IRLS,
and each inference engine has its own E method, so the code is fully modular.)
Sequential/batch Bayesian parameter learning (for fully observed tabular nodes only).
BNT supports several methods for regularization, and it is easy to add more.
Any node can have its parameters clamped (made non-adjustable).
Any set of compatible nodes can have their parameters tied (c.f., weight sharing in a neural net).
Some node types (e.g., tabular) supports priors for MAP estimation.
Gaussian covariance matrices can be declared full or diagonal, and can be tied across states of their discrete parents (if any).
BNT supports several methods for structure learning, and it is easy to add more.
Bayesian structure learning, using MCMC or local search (for fully observed tabular nodes only).
Constraint-based structure learning (IC/PC and IC*/FCI).
Tabular (multinomial)
Gaussian
Softmax (logistic/ sigmoid)
Multi-layer perceptron (neural network)
Noisy-or
Deterministic
BNT supports decision and utility nodes, as well as chance nodes, i.e., influence diagrams as well as Bayes nets.
BNT supports static and dynamic BNs (useful for modelling dynamical systems and sequence data).
BNT supports many different inference algorithms, and it is easy to add more.
BNT supports several methods for parameter learning, and it is easy to add more.
Batch MLE/MAP parameter learning using EM. (Each node type has its own M method, e.g. softmax nodes use IRLS,
and each inference engine has its own E method, so the code is fully modular.)
Sequential/batch Bayesian parameter learning (for fully observed tabular nodes only).
BNT supports several methods for regularization, and it is easy to add more.
Any node can have its parameters clamped (made non-adjustable).
Any set of compatible nodes can have their parameters tied (c.f., weight sharing in a neural net).
Some node types (e.g., tabular) supports priors for MAP estimation.
Gaussian covariance matrices can be declared full or diagonal, and can be tied across states of their discrete parents (if any).
BNT supports several methods for structure learning, and it is easy to add more.
Bayesian structure learning, using MCMC or local search (for fully observed tabular nodes only).
Constraint-based structure learning (IC/PC and IC*/FCI).
Loading .....
Recently Viewed Categories
Recently Viewed Products
