The basic use of a graphical model is to perform inference: making predictions about the values of unobserved variables, conditioned on the values of observed variables and the parameters.
In FACTORIE terminology, inference is a process which takes a list of variables and a model and produces a Summary, which is a container of marginals which also has a marginalization constant, referred to as logZ.
The main traits involved in the process of inference in FACTORIE are Marginal, FactorMarginal, Summary, and Infer.
The main byproduct of inference in FACTORIE are marginals over random variables. FACTORIE does not specify much in the Marginal trait itself, only that a Marginal should be over some variables and have a method to set those variables to their highest-probability values according to those marginals.
The most common type of Marginal in factorie is the DiscreteMarginal1, which represents a marginal over a single varying DiscreteVar. To do so it stores a tensor with the marginal probability of each value of the variable, in its proportions method. If doing MAP inference this tensor is a singleton tensor with only one non-zero entry, representing the fact that this marginal does not represent any uncertainty.
Another equally important byproduct of inference in FACTORIE is marginals over Factors. These FactorMarginals are mainly used for learning, as the gradient of all objectives for training graphical models involve a difference between target and expected sufficient statistics of the factors under some distribution.
All that the FactorMarginal trait provides is a pointer to Factor whose marginal it stores and a tensor representing the expected sufficient statistics of that Factor.
FACTORIE has automatic functionality to build FactorMarginals from simpler Marginals, in classes such as DiscreteMarginal1Factor2, which represents a marginal over one discrete variable and a factor marginal over a factor which touches that variable and another.
In FACTORIE a Summary is the main way to obtain Marginals and FactorMarginals. A Summary is an object which, given a variable or a factor can produce its Marginal or FactorMarginal. Summaries also store the normalization constant produced during inference, in its method logZ, which is necessary for learning.
FACTORIE provides some pre-implemented instances of Summary, most notably the MAPSummary, which can store the results of MAP inference given an Assignment and a set of Factors.
The main interface for inference-capable objects is the trait Infer, which defines a single method, infer(), taking a list of variables, a model, and a preexisting Summary (to deal with variables which are being marginalized in EM), and produces a Summary.
It is easy to implement your own inferencer. The most convenient way to do so is by reusing one of the existent types of Summary, such as the MAPSummary for MAP inference, and filling it in as needed.
Maximize in FACTORIE is a subtype of Infer which represents MAP inference. It adds no functionality except a maximize() method which calls infer and then calls setToMaximize on the returned Summary.
FACTORIE has implementations of many common inference algorithms.
In general most objects whose name start with InferBy are marginal inference algorithms, and similarly objects with names starting with MaximizeBy are MAP inference algorithms.
There are many variants belief propagation (BP), for both marginalization and maximization, including InferByBPChain, InferByBPTree, InferByBPLoopy, and InferByBPLoopyTreewise. Each of these has an analogous Maximize version.
There is also InferByGibbsSampling, and InferByMeanField, which marginalize using sampling and a mean-field variational algorithm for discrete variables.
For maximization we also have MaximizeByIteratedConditionalModes, which is the maximization analogue of Gibbs sampling, and MaximizeByMPLP, which uses dual coordinate ascent on the LP relaxation of inference.
Finally EM and Dual Decomposition are implemented outside of the inference API, by the EMInferencer and DualDecomposition classes.