Hugin Researcher

Category Intelligent Software>Bayesian Network Systems/Tools

Abstract The Hugin Researcher package contains a comprehensive, flexible and user friendly graphical user interface and the advanced Hugin Decision Engine for application development. The user interface contains a graphical editor, a compiler and a run-time system for the construction, maintenance and usage of knowledge bases using Bayesian network technology.

The Hugin Decision Engine (HDE) contains all the functionality related to handling and using 'knowledge bases' in a programming environment. The HDE is delivered with application program interfaces (API's) for four (4) major programming languages: C, C++, Java, .NET and an ActiveX-server for e.g. Visual Basic.

Hugin Researcher is for employees or students at an academic facility. This product is for users who wish to utilize Bayesian technology to build advanced applications for research purposes.

The Hugin Researcher package comes as either a 32-bit or 64-bit application (the ActiveX-server is only 32-bit).

The Feature List for HUGIN Decision Engine 6.7 - HDE 6.7 and the Feature List for HUGIN Graphical User Interface (HGUI) 6.9 - HGUI 6.9 are very similar and are therefore detailed below only once, contact the manufacturer for additional features/capabilities of the HUGIN Graphical User Interface:

Construction --

Construction of Bayesian network, chain graph (only available through the Hugin Network File Format) and influence diagram models (known as domains within the Hugin Decision Engine).

1) Creating/cloning/deleting of domains/nodes;

2) Adding/removing edges;

3) Accessing/changing the number of states of a node.

4) Discrete chance and decision nodes with subtypes labeled, Boolean, numbered and interval;

5) More than one utility node (assumes an additively decompositing utility function);

6) Conditional Gaussian distributed variables;

7) Direct specification of conditional probability distributions, conditional Gaussian densities and utility functions;

8) Probabilistic relations and utility functions can be specified by mathematical and logical descriptions using the Table Generator;

9) Substitution of the class of instance nodes;

10) Substitution of node parents.

Table Generator supporting --

1) Discrete distributions (Binomial, Negative Binomial, Poisson, Geometric, NoisyOr, Distribution).

2) Continuous distributions (Normal, Beta, Gamma, Exponential, Weibull, Uniform, PERT, Triangular, Log-Normal) including a truncation operator.

3) Logical, conditional, and comparison operators (and, or, not, if-then-else, equals, less than, greater than, not equals, less than or equals, greater than or equals).

4) Standard mathematical operators (add, subtract, multiply, divide, power, negate, min, max, log, exp, sqrt, log2, log10, sin, cos, tan, cosh, sinh, tanh, abs, ceil, floor, and mod).

5) Tables only need to be generated once, but can be generated on demand.

6) The number of values for each interval parent node used when generating the table of a child can be specified through the Application Programming Interface to the Hugin inference engine.

The PC algorithm for learning the graphical structure of a Bayesian network from a database of cases based on statistical test of dependence relations.

The specification of domain expert knowledge when performing structural learning.

EM-learning of a subset or all of the conditional probability distributions in Bayesian networks with discrete variables. Prior knowledge can be specified both in order to speed up the learning and to guide the learning algorithm (penalized-EM).

EM-learning in object-oriented Bayesian networks. This feature enables the user to exploit the composition of an object-oriented Bayesian network when estimating conditional probability tables from data.

File support for the Hugin Network Language, the Hugin Knowledge Base format and the compressed Hugin Knowledge Base format.

Support for passwords on Hugin Knowledge Base (hkb) files.

Compilation of domains into junction forests (i.e. a set of junction trees). A number of different algorithms for constructing the junction tree of cliques: clique size, clique weight, fill in size, and fill in weight, total weight (optimal) and user specified. A compilation log can be associated with domains. Node elimination orders may be saved to and loaded from file.

Conditional probability distributions, density functions and utility functions can be changed and used without performing a new compilation of the junction tree.

Usage --

The insertion of both hard (instantiation) and soft (likelihood) evidence.

The Hugin inference engine exploits the cache in modern Central Processing Units (CPUs) to speed up inference.

Retraction of evidence; Evidence is Not removed from the domain when the domain is un-compiled. Also, evidence can be entered and retracted when the domain is Not compiled.

Retrieve state index from value or label; Retrieve table index from a configuration of its nodes and vice versa.

Propagation of evidence in junction tree of cliques --

1) Sum and max propagation in normal and fast-retraction mode with discrete variables.

2) Sum-propagation of evidence in normal mode with a mixture of Conditional Gaussian and discrete variables (exact computations).

3) The probability of the propagated evidence is available as a result of propagation (normalization constant).

The computation of the joint probability distributions of a set of discrete variables, the joint density of continuous variables, and a mixture of these.

Retrieve the belief of a discrete node and the density of conditional Gaussian node.

Solution of sequential decision problems with multiple utility nodes and missing no-forgetting links; Retrieve expected utility of a decision node.

Sampling using the Bayesian network or the junction tree of cliques and sampling of discrete, conditional Gaussian, and mixture of conditional Gaussian and discrete variables conditional. A configuration of the variables can be sampled according to the distribution determined by the evidence.

Sampling in un-compiled Bayesian networks when the set of nodes with evidence form an ancestral set of instantiated nodes.

Zero-compression; Approximation; Save to memory functionality to support efficient inference and initialization.

Parallel processing for probabilistic inference on multi-processor systems.

Junction tree navigation; Save the junction tree and evidence entered into the junction tree in hkb-file.

Revision –

Sequential learning of a subset of the conditional probability distributions (adaptation). Also, available in influence diagrams. Beliefs in conditional probability tables can be specified using experience counts and the impact of prior knowledge can be faded away using fading factors.

Arc-reversal; Rearrange content of conditional probability distributions and utility functions.

Analysis --

Parameter sensitivity analysis; Conflict analysis; Value of information analysis on discrete random variables.

Possible suspicious findings can be located using fast retraction of evidence propagation; Functionality to support implementation of sensitivity analysis; d-separation/connection analysis.

Documentation --

User defined attributes on nodes and domains; User data on nodes and domains; Readable network file format.

Possibility of changing name, label, position and size of nodes; Many help facilities in the form of an extensive reference manual.

The Hugin Decision Engine is provided in two versions: a version using single precision floating point arithmetic and a version using double- precision floating point arithmetic. The double precision version may prove useful in computations with continuous random variables (at the cost of a larger space requirement).

Domains saved in the hkb-format using a single precision version can be loaded by a double-precision version of the Hugin Decision Engine, and vice versa. Error handling: whenever a Hugin Decision Engine operation fails, an error indicator is returned - it is then up to the application to decide what action to take.

32-bit and 64-bit APIs (except the Visual Basic API) are available for all platforms except MAC OS X.

System Requirements

Windows

Solaris

Linux

Mac

Manufacturer

Manufacturer Web Site Hugin Researcher

Price Contact manufacturer.

G6G Abstract Number 20163

G6G Manufacturer Number 101230