At its heart FACTORIE is a toolkit for graphical models. All its specific applications, including their data representation, inference and learning methods are built on a small set of common graphical model primitives.

Introduction to Graphical Models

Graphical models are a formalism in which a graph denotes the conditional dependence structure between random variables. The formalism is the marriage between probability theory and graph theory. It provides an elegant framework that combines uncertainty (probabilities) and logical structure (independence constraints) such that complex joint probability distributions over multiple variables that would have otherwise been intractable to represent or manipulate can instead be represented compactly and often manipulated efficiently. Since graphical models can straightforwardly express so many different probabilistic models, they have become a lingua-franca for statistics, machine learning, and data mining.

In graphical models, variables are depicted by the nodes a graph, drawn as circles, and dependencies among variables are depicted by edges, drawn either as directed (with arrows), or undirected (without arrows).

There are two main types of graphical models.

*Directed graphical models (also known as Bayesian networks) represent a joint distribution over random variables by a product of normalized conditional probability distributions (one for each “child” variable, conditioned on the other “parent” variables that are connected by an incoming directed edge). They are convenient generative models when variable values can be generated by an ordered iteration of the graph nodes from “parent” variables to “child” variables.

*Undirected graphical models (also known as Markov random fields) represent a joint distribution over random variables by a product of unnormalized non-negative values (one value for each clique in the graph). They are convenient models for data in which it is not intuitive to impose an ordering on the variables’ generative process. They can represent different patterns of independence constraints than directed models can, and vice versa—neither one is strictly more expressive than the other.

*Factor graphs are a generalization of both directed and undirected graphical models, capable of representing both.

When drawn, factor graphs depict variables as circular nodes in the graph (just as in directed and undirected graphical models), however rather than having edges that connect variables directly to each other, edges instead connect variables to factors (which are drawn as black squares). In other words, variables are connected to other variables only through factors. The variables connected to a factor are called the “neighbors” of the factor. A factor graph is a bipartite graph connecting variables and factors.

Factor graphs represent a joint distribution over random variables by a product of (normalized or unnormalized) non-negative values—one value for each factor in the graph. The factors can be understood as “compatibility functions” that take as input the values of the variables which they neighbor, and outputing a “compatibility score” such that higher scores indicate the combination of values is more likely, and lower scores indicate the combination of values is less likely. A score of 0 indicates the combination is impossible.

Directed graphical models can be represented by having one factor for each (child) variable, where the factor is also connected to the other (parent) variables on whose values we must condition when generating the child’s value. The factor’s scores are normalized probabilities.

Undirected graphical models can be represented with one factor per clique (or other arbitary subsets of clique members). The factors’ scores are unnormalized non-negative values, and thus in order to obtain a normalized joint probability distribution over all the variables in the model, we must normalize the product of factor scores by the proper normalization value (called the “partition function”).

In practice, most implementations of graphical models use the log of the score values so that they can be summed rather than multiplied, and a larger dynamic range of scores can be represented with limited floating-point precision in hardware. Naturally these log-scores can then vary from negative infinity (indicating an impossible combination of values in the neighbors) to positive infinity (indicating an obligatory combination of values in the neighbors). Since the use of log-scores is so common (and used in our implementation) in the remainder of this documentation we will now simply use the term “score” to refer to log-scores.

Factor graphs in FACTORIE

The FACTORIE library defines Scala classes for representing the elements of a factor graph. Usually the names of our Scala classes and methods are simply the standard names used in English machine-learning or statistics vocabulary.

In this section we further describe factor graphs while introducing FACTORIE class and method names indicated in mono-space font. Class (and trait) names are capitalized. Method names begin with a lowercase letter.

We provide here a high-level introduction to FACTORIE’s identifiers. Detailed explanations with code examples are given in subsequent chapters.

FACTORIE modular design philosophy

FACTORIE is explicitly designed to support independent, non-intertwined definitions of

  1. data representations with variables,
  2. models (value preferences, distributions, dependencies) with factors,
  3. inference methods, and
  4. parameter estimation.

This separation provides FACTORIE users with great flexibilty to mix-and-match different choices in each of these four dimensions. For example, the data representation for some task may be written just once, while different choices of dependencies are explored for this task—there is no need to re-write the data representation for each experiment with a different model. Similarly, inference methods may be selected separately from the model definition.

Representing data with variables and values

The data constituting any machine learning task are represented by variables. A variable can be understood as a container for a value. A variable may also have certain relations to other variables (for example, by being part of a sequence of variables corresponding to a sequence of part-of-speech tags). FACTORIE has many classes for representing different types of variables. The root of this class hierarchy is the abstract trait Var.

An Assignment represents a mapping from variables to values, and can be treated as a function. The value of a variable v1 in assignment a2 can be obtained by a2(v1).

Note that an assignment stores a single value for a variable, not a probability distribution over values. (Of course FACTORIE has extensive facilities for representing distributions over values, but these are not intrinsic to a variable definition or assignment—rather they are the purview of inference, because different inference methods may choose to use different distribution representations for the same variable.)

There are multiple concrete sub-classes of Assignment. A MutableAssignment can be modified. A TargetAssignment returns the gold-standard (e.g. human-labeler-provided) value for a variable when such a labeled value is available. An Assignment1 is a compact efficient representation for the value assignment of a single variable (similarly Assignment2..4 also exist). A HashMapAssignment is an arbitrarily-sized mutable assignment with storage based on a HashMap.

Since we want to efficiently support the common case in which many variables have relatively static value or unique assignments (because they are observed, or because they are being sampled in a single MCMC inference process) FACTORIE also has the GlobalAssignment object. There may exist multiple instances of the other assignment classes, each with its own values; however, there is only one GlobalAssignment. For efficiency the GlobalAssignment values are not stored in the GlobalAssignment, but rather in the corresponding variable instances themselves.

All variables have a value method that returns the variable’s currently assigned global value. In other words for variable v1 GlobalAssignment(v1) is equivalent to v1.value. Henceforth when we refer to “a variable’s value” we mean “a variable’s globally-assigned value”.

All variables also have methods for testing the equality (===) or inequality (!==) of their global values. Thus v1 === v2 will return true if variables v1 and v2 have the same values, and is a simple shorthand for v1.value == v2.value. (Traditional equality (with == or equals) on variables always tests the equality of the system identity of the two variables, not their values.)

If some non-global assignment a2 does not contain a value for v1 then looking up the value with a2(v1) may have different behavior in different assignment subclasses. For example, a HashMapAssignment will throw an error, but an Assignment1 (or Assignment2..4) will simply return the variable’s globally-assigned value.

Many types of variables

FACTORIE has different subclasses of Var for holding values of different types. There are a large number of traits and classes for variables. The following naming convensions make their interpretation easier. All abstract traits and classes end in Var, while concrete classes end in Variable. Almost all classes ending in Variable have an abstract Var counterpart that does not necessarily specify the mechanism by which the variable stores its value in memory.

The type of the value is named immediately before the Var or Variable suffix (for example, IntegerVariable has values of type Int). Further modifiers may appear as prefixes.

All variables also have a member type Value indicating the Scala type returned by its value method. In some variables the Value type is bound, but not assigned. In other cases it is assigned.

Some variables have mutable value (inheriting from the trait MutableVar). Almost all classes ending in Variable are mutable. Mutable variable values can be set with the := method. For example, if v1 has integer values, we can set the value of v1 to 3 by v1 := 3.

The following is a selection of FACTORIE’s most widely-used variable classes.

has value with Scala type Int. DoubleVariable
has value with Scala type Double. TensorVariable
has value of type Tensor, which is defined in the FACTORIE linear algebra package cc.factorie.la. This variable class makes no restrictions on the dimensionality of the tensor, nor the lengths of its dimensions. VectorVariable
has value of type Tensor1, which is a one-dimensional Tensor (also traditionally called a “vector”). In addition each VectorVariable is associated with a DiscreteDomain (further described below) whose size matches the length of the variable’s vector value. DiscreteVariable extends VectorVar
has a value among N possible values, each of type DiscreteValue, and each associated with an integer 0 through N-1. This DiscreteValue inherits from Tensor1 and can also be interpreted as a “one-hot” vector with value 1.0 in one position and 0.0 everywhere else. Given a DiscreteValue dv1 its integer value can be obtained by dv1.intValue. The length of the vector (in other words, the value of N) can be obtained by dv1.length. CategoricalVariable[A] extends DiscreteVar
has value among N possible values, each of type CategoricalValue[A] (which inherits from DiscreteValue), each associated with an integer 0 through N-1, and also associated with a “category” (often of type String). These variables are often used for representing class labels and words, when a mapping from String category names to integer indices is desirable for efficiency (such as indexing into an array of parameters). Given a CategoricalValue[String] cv1 its integer value can be obtained by cv1.intValue and its categorical (String) value can be obtained by cv1.categoryValue. Its value may be set with an integer: cv1 := 2 or set by category string: cv1 := "sports". (The mapping between Strings and integers is stored in a CategoricalDomain, which is described below.) CategoricalVectorVariable[A] extends VectorVar
has value of type Tensor1, which is a one-dimensional Tensor. In addition each CategoricalVectorVariable is associated with a CategoricalDomain[A], which stores a mapping between values of type A (e.g. String) and integers. Thus each position in the vector is associated with a category. This variable type is useful for storing bag-of-words counts, for example. BooleanVariable extends CategoricalVar[Boolean]
has one of two possible values, each of type BooleanValue (which inherits from CategoricalValue[Boolean]), one of which is associated with integer 0 and boolean value false, the other of which is associated with integer value 1 and boolean value true. Given a BooleanValue bv1 its integer value can be obtained by bv1.intValue and its boolean value can be obtained by bv1.booleanValue. MassesVariable extends TensorVar
has value Masses, which are Tensors constrained to contain non-negative values. Masses are useful as the parameters of Dirichlet distributions. ProportionsVariable extends MassesVar
has value Proportions, which are Masses constrained to sum to 1.0. Proportions are useful as the parameters of discrete or multinomial distributions. RealVariable extends VectorVar
has a single real scalar value, stored in an object of type RealValue (which inherits from Tensor1). This variable is similar to DoubleValue in that it stores a scalar value, however since its value type inherits from Tensor1, it can be used in dot products.

All of the above variable classes have constructors in which their initial value may be set. For example, new IntegerVariable(3) will create a new variable whose initial value is 3.

Some of the above variable types have specializations for the case in which human-labeled gold-standard target values are known. These specializations inherit from the LabeledVar trait which provides a method targetValue that returns this gold-standard target value. The target value is stored separately and may be different from the variable’s current global assignment value. For example, if i1 is a LabeledIntegerVariable we can determine if the variable is currently assigned to its gold-standard target value by evaluating the boolean expression i1.value == i1.targetValue. (The method valueIsTarget is a convenient short-hand for this test.) In almost all cases the target value is initialized to be the value provided in the variable constructor.

Common LabeledVar sub-classes include:

LabeledDiscreteVariable LabeledCategoricalVariable[A] LabeledBooleanVariable LabeledStringVariable LabeledIntegerValue LabeledDoubleValue LabeledRealValue

All the above variable types are common in existing graphical models. However, FACTORIE also has random variables for representing less traditional values. Although these may seem like peculiar value types for a graphical model, they nonetheless can be scored by a factor, and are often useful in FACTORIE programs.

has value with Scala type String. SeqVariable[A]
has value of type Seq[A], that is, a sequence of objects of type A. ChainVariable[A] extends SeqVar[A]
has value of type Seq[A], but the elements A must inherit from ChainLink which have next and prev methods. SpanVariable[A] extends SeqVar[A]
has value of type Seq[A], and is a subsequence of a ChainVar[A]. SetVariable[A]
has value of type Set[A], that is, an unordered set of objects of type A. RefVariable[A]
has value of type A. In other words, it is a variable whose value is a pointer to a Scala object. EdgeVariable[A,B]
has value of type Tuple[A,B], that is a pair of objects: a “source” of type A and a “destination” of type B. ArrowVariable[A,B] extends EdgeVar[A,B]
like EdgeVariable has value of type Tuple[A,B], but only the “destination” is mutable, while the “source” is immutable.

Variable domains

Some (but not all) FACTORIE variables have a corresponding domain object that contains information about the set of values the variable may take on. Such variables inherit from VarWithDomain which provides a domain method that returns the variable’s domain.

For example, DiscreteVariable subclasses have a corresponding DiscreteDomain whose main role is to provide its size—the number of possible values the variable can take on, i.e. from 0 to N-1.

CategoricalVariable[A] subclasses have a corresponding CategoricalDomain[A] (inheriting from DiscreteDomain) which provides not only its size but also a one-to-one mapping between the categories and the integers 0 to N-1. For example, if cd1 is a CategoricalDomain[String] then cd1.category(3) returns the String corresponding to index 3 in the domain. cd1.index("apple") returns the integer index corresponding to the category value "apple". If "apple" is not yet in the domain, it will be automatically added, assigned the next integer value, and the size of the domain will be increased by one (assuming that cd1 has not previously been frozen).

Undoable changes

FACTORIE also provides the ability to undo a collection of changes to variable values. (This is especially useful in Metropolis-Hastings inference, in which a proposed change to variable values is made, but may be rejected, requiring a reversion to previous values.)

A Diff instance represents a change in value to a single variable. It has methods undo and redo, as well as the variable method which returns the variable whose value was changed.
A DiffList stores an ordered list of Diffs.

The := method for assigning the value of a MutableVar was described above. An alternative method for changing the value of a MutableVar is set, which, in addition to the new value, also takes a DiffList as an implicit argument. The set method will then automatically construct a Diff object and append it to the given DiffList.

Expressing preferences with factors and models

A collection of variables and their values represents a possible state of the world. To express a degree of preference for one possible world over another—or a probability distribution over possible worlds—we need a mechanism for scoring the different worlds, such as a collection of factors in a factor graph.


In FACTORIE, factors are represented by instances of the trait Factor. All Factors have certain basic methods. The numVariables method returns the number of variables neighboring the factor. The list of neighboring variables themselves is returned by the method variables. Calling currentScore calculates the factor’s score for the neighboring variables’ values in the current global assignment. You can obtain the score under alternative assignments by calling assignmentScore(a1), where a1 is some Assignment.

Subclasses of the Factor trait include Factor1, Factor2, Factor3, and Factor4, which have one, two, three or four neighboring variables respectively. (FACTORIE does not currently support factors with more than four neighbors; if necessary these could be added in the future, however, thus far we have not found them necessary because (a) the use of TensorVariable, SeqVariable and other composite-valued variables handle cases in which many values are necessary, or (b) the use of a “var-args” ContainerVariable helps similarly.)

The only abstract method in Factor1 (and its other numbered relations) is score, which takes as arguments the values of its neighbors and returns a Double.

Some Factor subclasses define a statistics method that takes as arguments the values of its neighbors and returns the sufficient statistics of the factor. (Often the sufficient statistics will simply be the combination of neighbor values, but this method provides users with an opportunity to instead perform various useful transformations from values to sufficient statistics.) In these cases the score method is then defined to be the result of the statisticsScore method, whose only argument is the statistics, thus ensuring that the factor score can be calculated using only the sufficient statistics.

For example, in the class DotFactor2 the abstract method statistics must return sufficient statistics of type Tensor. The statisticsScore method is then defined to be the dot-product of these sufficient statistics with another Tensor of scoring parameters, which is returned by the abstract method weights.

The DotFactorWithStatistics2 class inherits from DotFactor2 but requires that both its neighbors inherit from TensorVar. It then defines its statistics method to return the outer-product of the tensor values of its two neighbors.

The naming convention is that classes having the suffix WithStatistics define the statistics method for the user. Most other classes have an abstract statistics method that must be defined by the user.

Templated Factors

In many situations we want to model “relational data” in which the variables and factors appear in repeated patterns of relations to each other.

For example, in a hidden Markov model (HMM) there is a sequence of observations (random variables with observed values) and a corresponding sequence of hidden states (random variables whose value is the identity of the state of a finite-state machine that was responsible for generating that observation). Each hidden state variable has “next” and “previous” relations in the chain of hidden states. Each also has a corresponding observation variable (the one that generated it, and occurs in the same sequence position).

The standard HMM is “time invariant” (sometimes called “stationary”), meaning that the hidden state-transition probabilities and the observation-from-state generation probabilities do not depend on their position in the sequence. Thus, although each transition needs its own factor in the factor graph (because each factor has different neighboring variables), each of these factors can share the same scoring function.

In FACTORIE factors that share the same sufficient statistics function, score function, and other similar attributes are said to belong the same “family” of factors. The trait Family defines a Factor inner class such that its instances rely on methods in the Family class for various shared functionality.

For example, the DotFamily provides a weights method for accessing the dot-product parameters shared by all member factors.

Analogous to the numbered Factor subclasses there are numbered families, Family1, Family2, Family3, Family4, each defining an inner factor classes with the corresponding number of neighbors.

A Template is a subclass of Family that is also able to automatically construct its factors on the fly in response to requests to “unroll” part of the graphical model. A Template’s unroll method takes as input a variable, and uses its knowledge about the “pattern of relational data” to create and return the Template’s factors that neighbor the given variable.

Note that for factors with more than one neighbor this requires traversing some relational pattern to find the other variables that are also neighbors of these factors. This knowledge is flexibly encoded in the implementation of numbered “unroll” methods. For example Template2 implements its unroll method in terms of two abstract methods: unroll1 and unroll2. The unroll1 method takes as input a variable with type matching the first neighbor of the factor, and is responsible for creating and returning a (possibly empty) collection of templated factors that touch this variable—finding the second neighbor in each case by traversing some relational structure among the variables.

Implementing position-specific unroll methods may seem cumbersome, but in most cases these method definitions are extremely succinct, and moreover the ability to specify the relational pattern through arbitrary Turing-complete code provides a great deal of flexibility. Nonetheless, in the future, we plan to implement a simpler template language on top of this unroll framework.

As with Family there are numerous specialized subclasses of Template. For example a DotTemplateWithStatistics3 is a template for a factor with three neighboring variables, each of which must inherit from TensorVar; the template defines a factor scoring function by the dot-product between the outer-product of the three variables and the template’s weights. The only abstract methods are unroll1, unroll2, unroll3 and weights.


For the sake of flexible modularity, in FACTORIE, a “model” does not include the data, or inference method, or learning mechanism—a model is purely a source of factors. The key method of the Model class is factors which takes as input a collection of variables, and returns a collection of Factors that neighbor those variables. (This definition allows efficient implementation of large relational models in which factors are created on-the-fly in response to specific requests instead of needing to be “unrolled” entirely in advance.)

The Model trait also provides alternative factors methods for obtaining the factors that neighbor an individual variable, a Diff or a DiffList. There are also methods providing a filtered collection of factors, such as factorsOfClass, factorsOfFamily or factorsOfFamilyClass. For example, the later may be used to obtain only those factors that are members of the DotFamily class, and thus conducive to gradient ascent parameter estimation.

Model also has convenience methods that return the total score of all factors neighboring the method arguments. For example, its assignmentScore method returns the sum of the factors’ scores according to the given assignment. Similarly currentScore does so for the global assignment.

There are several concrete subclasses of Model. A TemplateModel’s factors come entirely from a collection of Templates. By contrast, an ItemizedModel stores a set of factors created beforehand. A CombinedModel returns the union of the sets of factors returned by multiple submodels. There are also specialized models, such as the ChainModel for linear-chain conditional random fields, with specialized inference and learning algorithms.

Parameters and Weights

The situation in which templated factor scores are calculated by dot-products is so common (and so relevant to our typical parameter estimation procedures) that FACTORIE provides special support for this case.

In a templated model the parameters are a set of weights tensors, one per factor template. The Weights trait is a TensorVar that holds the parameters of one family. For example, DotFamily has a weights method that returns the Weights used to calculate its factor scores.

A WeightsSet is then a collection of Weights, typically holding all the parameters of a model (across multiple families) based on dot-products.

To declare that an object has a WeightsSet holding parameters we use the Parameters trait, which defines a parameters method returning the WeightsSet. (Thus this trait should not be thought of as “being” parameters, but “holding” parameters.) Its typical use-case is Model with Parameters, declaring that the object is a source of factors, and also has a WeightsSet available through its parameters method.

During parameter estimation one typically needs additional tensors having the same structure as the parameters, but that hold other quantities, such as gradients or expectations. For example the blankSparseMap method on WeightsSet will create a “blank” (initially zero-filled) WeightsMap, which is a collection of tensors having the same structure as the WeightsSet that created it. This WeightsMap can then be filled with a gradient through some computation on training data. It is called a “map” because the parameter-value-containing Weights objects are used as “keys” to obtain the gradient tensor corresponding to that Weights object.

In summary, both WeightsSet and WeightsMap make available a collection of Tensors. In WeightsSet the tensors are stored inside the constituent Weights (TensorVars) themselves. In WeightsMap the tensors are stored in a map internal to the WeightsMap; they are accessible by lookup using the corresponding Weights as keys.

The abstract superclass of both WeightsSet and WeightsMap is TensorSet. Although a TensorSet is not a single tensor but a collection of tensors, it has a variety of useful methods allowing it to be treated somewhat like a single tensor. These methods include oneNorm and twoNorm for calculating norms, += for incremental addition, dot for the sum of dot-product of all constituent tensors, and different to determine if the element-wise distance between two WeightsSets is larger than a threshold.

Searching for solutions with inference

Once we have a way to represent a possible world (variables, with their possible values) and a way to represent preferences over different possible worlds (factors provided by a model) we can address the problem of searching for the most preferred possible world (MAP inference) or finding the probabilities of subsets of all possible worlds (marginal inference).

In FACTORIE terminology, inference is a process that takes as input a list of variables and a Model and produces as output a Summary, which contains a collection of Marginals and which also has a marginalization constant, accessible as logZ.


A Marginal represents a joint distribution over a subset of the variables. For example, a DiscreteMarginal2 specifies two DiscreteVars, and a Proportions2 which contains their marginal distribution. A RealGaussianMarginal1 represents a univariate Gaussian distribution with a specified mean and variance. Naturally a marginal that results from maximum aposteriori inference will simply have a spike with all probability on the maximum-scoring values. The most widely used marginals are those over a single variable, inheriting from Marginal1 because marginals over multiple variables more typically represented by FactorMarginals.

A FactorMarginal is marginal associated with a Factor (note, however, that for technical reasons it does not inherit from the Marginal trait). These are mainly used for learning, providing a tensor of sufficient statistics which are used to calculate parameter gradients.

Its marginal distribution may cover all the neighboring variables of the factor, or, in the case in which inference varied some but not all of the neighbors, the marginal distribution will be over a subset of the neighboring variables of the factor. Its tensorStatistics method returns the expected sufficient statistics of the factor, which always covers all the neighboring variables of the factor; this is useful for parameter estimation.

There is infrastructure for automatically creating a FactorMarginals from Marginals that cover a subset of the neighbors of a Factor. For example if we want a FactorMarginal for a Factor2 in which one of the factor’s two neighbors is varying but the other is treated as constant, we can use a DiscreteMargina1 to construct a DiscreteMarginal1Factor2 whose sufficient statistics will cover both neighboring variables.

A request for inference on a model results in a Summary which is a collection of Marginals .


When inference is run the result is a Summary which is a container for (a) the collection of variables that varied during inference, (b) a collection of the corresponding single-variable Marginal1s, (c) a collection of FactorMarginals for the factors used during inference, and (d) a normalization constant, logZ. Using a Summary you can look up a Marginal1 given a variable, and can look up a FactorMarginal given a Factor.

Several specialized implementations of Summary are provided. For example MAPSummary puts all of its probability on one Assignment of the variables’ values. Because it also contains a collection of Factors and FactorMarginals, it can also be used for (max-margin) parameter estimation. Other examples include BPSummary for holding the intermediate and final states of belief propagation, and IndependentDiscreteSummary for holding an IID distribution over discrete variables.


The primary interface for making inference requests is in the method infer in the Infer trait. This method takes two or three arguments: (1) the collection of non-constant variables whose values should be varied and whose marginal distributions should be calculated; (2) the Model whose factors will score these different assignments; and optionally, (3) a Summary which may indicate additional variables that should be marginalized (and the functional form of that marginalization), or which may alternatively provide information about the functional form to be used for the marginals of the first argument variables.

The Maximize trait is a subtype of Infer for object that implement maximum aposteriori (MAP) inference. It adds a method maximize, which calls infer and then assigns the variables to their maximizing values.

Generally it is easy to implement your own inferencer and make it conform to this API (which then immediately enables its use for parameter estimation and other tasks). Typically much of the existing infrastructure for Summarys and Marginals will be helpful in your implementation.

FACTORIE comes with a large collection of already-implement marginal and MAP inference procedures, whose names start with InferBy MaxmimizeBy respectively.

There are many variants of belief propagation (BP) for both marginalization and maximization on graphical models of discrete variables. These include InferByBPChain, an efficient implementation of forward-backward in linear-chain structured models; InferByBPTree for exact inference tree-shaped graphical models; InferByBPLoopy for approximate BP inference by on graphical models with loops; InferByBPLoopyTreewise for more robust approximate BP inference with tree-reparameterization [Wainwright, Jaakkola, Wilsky, 2001].

Other variational inference methods exist as well, including InferByMeanField, an implementation of marginal inference by mean field for discrete variables; MaximizeByMPLP, which uses dual coordinate ascent on the LP relaxation for MAP inference.

FACTORIE also provides infrastructure for Monte Carlo inference. This includes a generic Sampler as well as its special-case subclass, a ProposalSampler which selects among a collection of Proposal objects (each of which provides a score and a DiffList for undoing and redoing the proposed changes). Examples of ProposalSampler include the GibbsSampler and MHSampler (Metropolis-Hastings), and IteratedConditionalModes which is the maximization analogue of Gibbs sampling.

These underlying sampling mechanisms are wrapped into objects that implement the Infer interface, providing InferByGibbsSampling, InferByMeanField, MaximizeByIteratedConditionalModels, MaximizeByMPLP, etc.

Finally, Expectation-Maximization (EM) and dual decomposition are implemented outside of the Infer API, by the EMInferencer and DualDecomposition classes.

There is currently limited support for inference in that directly leverages the special case of directed graphical models. We have preliminarily implemented a system of storing and selecting among a collection of “recipes” for handling different patterns of random variables and factor types (similar to BUGS), but only a few such “recipes” have been implemented so far. One of those cases, however, provides an extremely efficient collapsed Gibbs sampler for latent Dirichlet allocation (LDA)—the FACTORIE implementation of LDA is actually faster than the widely-used MALLET implementation. See the Section on “Topic Modeling” below.

FACTORIE does not currently provide automated methods of selecting the best inference method given a model and collection of variables. This is an area of future work.

Estimating parameters

(To be provided from Luke’s text.)

Application packages

In additional to its general support for factor graphs, FACTORIE provides pre-build models and other infrastructure for particular application areas.




This package is not yet implemented, but will be in the future.


Natural language processing

Topic Modeling