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Found 9 papers, 1 papers with code
Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
For large network data, we propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in the proposed model.
Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Series
Modeling count-valued time series has been receiving increasing attention since count time series naturally arise in physical and social domains.
Poisson-Gamma Dynamical Systems with Non-Stationary Transition Dynamics
Bayesian methodologies for handling count-valued time series have gained prominence due to their ability to infer interpretable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with noisy and incomplete count data.
A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs
In particular, we use a sequential latent variable model to learn a dependency graph between the observed dimensions, for each sub-interval.
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks
To that end, the observed randomness and spatial-correlations are captured by learning the drift and diffusion terms of the stochastic differential equation with a Gumble matrix embedding, respectively.
Estimating Latent Population Flows from Aggregated Data via Inversing Multi-Marginal Optimal Transport
In particular, we propose to estimate the transition flows from aggregated data by learning the cost functions of the MOT framework, which enables us to capture time-varying dynamic patterns.
Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis
Group factor analysis (GFA) methods have been widely used to infer the common structure and the group-specific signals from multiple related datasets in various fields including systems biology and neuroimaging.
Dependent Relational Gamma Process Models for Longitudinal Networks
Within the latent space, our framework models the birth and death dynamics of individual groups via a thinning function.
A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks
We present a probabilistic model for learning from dynamic relational data, wherein the observed interactions among networked nodes are modeled via the Bernoulli Poisson link function, and the underlying network structure are characterized by nonnegative latent node-group memberships, which are assumed to be gamma distributed.
Social and Information Networks
