Continuous inference for aggregated point process data

Towards a Unified Framework for Inference with Aggregated Relational Data

Aggregated Relational Data (ARD), originally introduced by Killworth et al. (1998) as “How many X’s do you know” survey questions, are a common tool for observing social networks indirectly. Previous methods for ARD estimate specific network features, such as overdispersion. We suggest a more general approach to understanding social structure using ARD based on a latent space framework. We firs...

Approximate Inference in Continuous Determinantal Point Processes

Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion. In machine learning, the focus of DPP-based models has been on diverse subset selection from a discrete and finite base set. This discrete setting admits an efficient sampling algorithm based on the eigendecomposition of the defining kernel matrix. Recently, there has been growing interest in usi...

Inference in continuous-time change-point models

We consider the problem of Bayesian inference for continuous-time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times. We propose exact inference and sampling methodologies for two specific cases where the discontinuous dynamics is given by a Poisson process and a two-state Markovian switch. We test the methodology on simulated data, and ...

Determinantal point process models and statistical inference

Statistical models and methods for determinantal point processes (DPPs) seem largely unexplored. We demonstrate that DPPs provide useful models for the description of repulsive spatial point processes, particularly in the ‘soft-core’ case. Such data are usually modelled by Gibbs point processes, where the likelihood and moment expressions are intractable and simulations are time consuming. We e...

Algorithms and inference for simultaneous-event multivariate point-process, with applications to neural data