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Feature Lists
Hugin Expert has incorporated new and exiting cutting edge features into our software packages. Below you find a comprehensive feature list of our graphical user interface and our decision engine.
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Feature List for HUGIN Graphical User Interface 6.9 - HGUI 6.9
Construction
Construction of Hugin Knowledge Bases
utilizing Bayesian network and influence diagram models (known as
domains within the Hugin Decision Engine):
- creation/deletion of
domains/nodes
- adding/removing edges
- accessing/changing the number of
states of a node
Construction
of hierarchical Bayesian network and influence diagram models. A
hierarchical model may contain other models as submodels. The
interface of a model consists of a set of input nodes and a set
of output nodes. This feature can be considered as
object-oriented Bayesian networks and influence diagrams without
inheritance.
Class
collections to store multiple OOBN definitions in the same file.
Discrete chance and decision nodes with
subtypes labelled, boolean, numbered and interval.
More than one utility node (assumes an
additively decompositing utility function).
Conditional Gaussian distributed variables
(exact inference).
Direct specification of conditional
probability distributions, conditional Gaussian densities and
utility functions.
Probabilistic relations and utility functions
can be specified by mathematical and logical descriptions using
the Table Generator.
Rotation of the interface of instance nodes.
Substitution of the class of instance nodes.
Substitution of node parents.
Continuous-discrete node value transfer function.
Usage
Table Generator supporting
- Discrete distributions (Binomial,
Negative Binomial, Poisson, Geometric, NoisyOr, Distribution)
- Continuous distributions (Normal,
Beta, Gamma, Exponential, Weibull, Uniform, PERT, Triangular,
LogNormal) including a truncation operator.
- 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)
- 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, mod)
- Tables need only be generated once,
but can be generated on demand. This includes tables for
individual nodes. The generated tables are stored in the Hugin
Network and the Hugin KB files in order to speed up compilation
of domains
- Specification of "number of
samples per interval" for each table.
The structure and conditional probability
distribution of a Bayesian network can be estimated from data
using the Learning Wizard.
Learning Wizard supporting:
- Data acquisition (database
connectivity via ODBC or JDBC, interface to Oracle databases,
and plain text files)
- Data preprocessing (e.g., state
relabeling and discretization of continuous variables)
- Specification of domain expert
knowledge (i.e., structural constraints including support for
adding constraints between a node and a subset of nodes)
- Structural learning using the PC
algorithm
- Structural learning using the NPC
algorithm with detection of minimal resolutions of ambiguous
regions
- A data dependency slider to detect
the relative strengths of links as found in the data,
- Generation of randomized conditional
probability distributions (used as part of EM-learning)
- Estimation of the conditional
probability distributions using the EM algorithm.
The PC or NPC
algorithm for learning the graphical structure of a Bayesian
network from a database of cases based on statistical test of
dependence relations.
EM-learning of a subset or all of the
conditional probability distributions in Bayesian networks, Mixed
Bayesian networks, or Influence Diagrams. Prior knowledge can be
specified both in order to speed up the learning and to guide the
learning algorithm (penalized-EM).
Cut-and-paste facilities for nodes, network
fragments and conditional probability tables.
File support for the Hugin Network Language
and the Hugin Knowledge Base format.
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, fill in weight and total weight
(optimal). 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.
Insertion of
both hard (instantiation) and soft (likelihood) evidence.
Evidence is not removed from the domain when
the domain is uncompiled. Also, evidence can be entered and
retracted when the domain is not compiled.
The Hugin inference engine exploits the cache
in modern CPUs to speed up inference.
Retraction of evidence.
Propagation of
evidence in junction tree of cliques
- Sum and max propagation in normal and
fast-retraction mode with discrete variables
- Sum-propagation of evidence in normal
mode with a mixture of Conditional Gaussian and discrete
variables (exact computations)
- The probability of the propagated
evidence is available as a result of propagation (normalization
constant)
Retrieve the
belief of a discrete node and the density of conditional Gaussian
node.
Different ways of displaying the belief of a
node e.g. monitor windows, listing of marginals, and mean and
variance of conditional Gaussian distributed variables or display
of the exact distribution.
Solution of sequential decision problems with
multiple utility nodes and missing no-forgetting links.
Different ways of displaying the expected
utility of a decision node (monitor windows or a list of nodes).
Generation of cases with missing values (MAR
and MCAR).
Zero-compression.
Approximation.
Save-to-memory.
Simulation of
all variables in a model.
Save the junction tree (without clique
tables) and evidence entered into the junction tree in hkb-file.
Revision
Sequential
learning of a subset of the conditional probability distributions
(adaptation) is 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.
Adaptation
wizard.
Arc-reversal
between pairs of discrete variables and pairs of continuous
variables.
Node absorption.
Rearrange content of conditional probability
distributions and utility functions.
Analysis
Parameter
sensitivity analysis.
Support for computing joint probability
distributions.
Possible suspicious findings can be located
using fast retraction of evidence propagation.
Support for data conflict analysis including
tracing conflicts and conflict resolution.
Hypothesis driven data conflict analysis and
hypothesis drive conflict analysis.
Support for detailed conflict analysis in the
junction tree.
Value of information analysis on discrete
random variables and discrete decision variables.
Sensitivity to evidence analysis on discrete
random variables including identification of min-max beliefs,
impact of subsets of evidence analysis, discrimination of
hypotheses, what-if analysis, and impact of findings analysis.
Convolution
and XOI loss model.
d-separation/connection analysis.
Data
analysis.
Case
manager.
Development and
Documentation
User defined
attributes on nodes and domains.
User data on nodes and domains.
Readable network file format.
Hyperlinks in Node and Domain descriptions
Zoom factors in both the Hugin Network
language and Hugin Knowledge base file formats.
Grouping of nodes.
Belief bars can be forces to render in
scientific notation.
Support for
unicode.
Internationalization (includes support for
English and Japanese languages)
Graph layout algorithm with support for
external algorithms
Possibility of changing name, label,
position, font, color and size of nodes.
Usage and network log information.
Import and export of tables.
Print tables, networks, probabilities and
junction trees on printer or to a file.
Automatic generation of HTML network
documentation
Many help facilities in the
form of html-code including drag'N'drop.
The Hugin GUI can be customized
to meet the preference of the user.
The Hugin GUI is available for a wide range
of platforms including Microsoft Windows platforms, Sun Solaris
(sparc and x86) and Linux Red Hat. The Hugin GUI comes as either
a 32-bit or 64-bit application.
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Feature List for HUGIN Decision Engine 6.7 - HDE 6.7
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).
- Creating/cloning/deleting of
domains/nodes
- Adding/removing edges
- Accessing/changing the number of
states of a node
Discrete
chance and decision nodes with subtypes labelled, boolean,
numbered and interval.
More than one utility node (assumes an
additively decompositing utility function).
Conditional Gaussian distributed variables.
Direct specification of conditional
probability distributions, conditional Gaussian densities and
utility functions.
Probabilistic relations and utility functions
can be specified by mathematical and logical descriptions using
the Table Generator.
Substitution of the class of instance nodes.
Substitution of node parents.
Table Generator supporting
- Discrete distributions (Binomial,
Negative Binomial, Poisson, Geometric, NoisyOr, Distribution)
- Continuous distributions (Normal,
Beta, Gamma, Exponential, Weibull, Uniform, PERT, Triangular,
LogNormal) including a truncation operator
- 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)
- 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)
- Tables only need to be generated
once, but can be generated on demand
- 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.
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
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
Insertion of
both hard (instantiation) and soft (likelihood) evidence.
The Hugin inference engine exploits the cache
in modern CPUs to speed up inference.
Retraction of evidence.
Evidence is not removed from the domain when
the domain is uncompiled. 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
- Sum and max propagation in normal and
fast-retraction mode with discrete variables
- Sum-propagation of evidence in normal
mode with a mixture of Conditional Gaussian and discrete
variables (exact computations)
- The probability of the propagated
evidence is available as a result of propagation (normalization
constant)
Computation of
the joint probability distributions of a set of discrete
variables, the joint density of continuous variables, and a
mixture.
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 uncompiled 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 available with a
C API, a C++ API, a Java API, a .NET API and as an
ActiveX-server.
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.
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