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Found 11 papers, 5 papers with code
Extended Stochastic Block Models with Application to Criminal Networks
The coexistence of these noisy block patterns limits the reliability of routinely-used community detection algorithms, and requires extensions of model-based solutions to realistically characterize the node partition process, incorporate information from node attributes, and provide improved strategies for estimation and uncertainty quantification.
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Multinomial probit models are routinely-implemented representations for learning how the class probabilities of categorical response data change with p observed predictors.
Scalable and Accurate Variational Bayes for High-Dimensional Binary Regression Models
Modern methods for Bayesian regression beyond the Gaussian response setting are often computationally impractical or inaccurate in high dimensions.
Methodology Computation
Bayesian cumulative shrinkage for infinite factorizations
There is a wide variety of models in which the dimension of the parameter space is unknown.
Methodology
A nested expectation-maximization algorithm for latent class models with covariates
We propose a novel nested expectation-maximization algorithm for latent class models with covariates which allows maximization of the full-model log-likelihood and, differently from current methods, is characterized by a monotone log-likelihood sequence.
Computation Methodology
Tractable Bayesian Density Regression via Logit Stick-Breaking Priors
There is an increasing interest in learning how the distribution of a response variable changes with a set of predictors.
Computation
Bayesian Learning of Dynamic Multilayer Networks
Our formulation characterizes the edge probabilities as a function of shared and layer-specific actors positions in a latent space, with these positions changing in time via Gaussian processes.
Locally Adaptive Dynamic Networks
Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals.
Locally Adaptive Bayesian Multivariate Time Series
In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process.
Nonparametric Bayes dynamic modeling of relational data
Symmetric binary matrices representing relations among entities are commonly collected in many areas.
Locally adaptive factor processes for multivariate time series
In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process.
