Estimating animal utilization distributions from multiple data types: A joint spatiotemporal point process framework

Session Introduction: Joint Learning from Multiple Types of Genomic Data

Joint Learning of Modular Structures from Multiple Data Types

A commonly used technique for understanding underlying dependency structures among objects is module networks by Segal et al., which assumes a shared conditional probability distribution for objects within one module. However, learning structures from object variables alone can lead to spurious dependencies and to avoid over-fitting, imposing structural assumptions may be required. We propose a...

Joint composite estimating functions in spatiotemporal models

Modelling of spatiotemporal processes has received considerable attention in recent statistical research. However, owing to the high dimensionality of the data, the joint modelling of spatial and temporal processes presents a great computational challenge, in both likelihoodbased and Bayesian approaches. We propose a joint composite estimating function approach to estimating spatiotemporal cova...

Estimating a State-Space Model from Point Process Observations

A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stim...

A Geostatistical Data Assimilation Approach for Estimating Groundwater Plume Distributions from Multiple Monitoring Events

Knowledge of the distribution of groundwater contaminant plumes is needed to avoid pumping contaminated water, assess past exposure to contamination, and design remediation schemes to contain or treat the contaminated area. In most field cases, however, contamination is discovered by a small number of fortuitously located wells, and the full distribution of the plume is never known. This paper ...