Description Usage Arguments Value Author(s) References Examples
Performs collapsed Gibbs sampling for the marginal-product mixture model (DuBois 2010). This function takes a dataset of observed relational events, perform inference, and return point estimates of the latent parameters using the state at the last iteration of Gibbs sampling.
The model is appropriate for analyzing event data in 1, 2, or 3-mode graphs. The main assumption is that each event belongs to a latent class. We use MCMC to explore the posterior distribution of these assignments given observed data. The resulting class assignments can be useful for exploratory data analysis and predicting missing or future data.
At the moment, the number of latent classes must be chosen a priori, though a DP version is in the works.
With the current implementation, each dimension is treated separately, e.g. for a given latent class, there will be a distribution over likely senders and a separate distribution over likely recipients, even if this involves the same set of actors.
1 |
dataset |
A Tx2 or Tx3 matrix of integers, each row corresponding to an event, each integer identifying the participating vertices. |
C |
Number of desired latent classes. |
dims |
Maximum value for each categorical random variable. |
priors |
|
niter |
Number of Gibbs iterations to perform. |
z.init |
vector of initial latent class assignments for all observations. |
assignments |
Latent class assignments for the T observations from the last iteration of the Gibbs sampler. |
params |
|
counts |
A list where each element is a matrix counting the number of times values of the corresponding dimension were observed and assigned to a particular latent class. (These matrices are used for estimates of phi.) |
dims |
Maximum value for each categorical random variable. |
Christopher DuBois (<email: duboisc@ics.uci.edu>)
Christopher DuBois and Padhraic Smyth. Modeling Relational Events via Latent Classes. Proceedings of the 16th ACM SIGKDD, 2010.
1 | ## See demo(synthetic) and demo(enron).
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