Talker:  Daichi Mochihashi (ISM)

Title: Multiresolution Log Gaussian Cox point processes

Abstract: Inferring latent intensities of Poisson point processes is of fundamental importance in many fields such as seismology, ecology, neuroscience, and activity recognition, to name a few. However, so far only quite simple models have been introduced for inferring the intensity, such as homogeneous Log  Gaussian Cox processes (Moller+, 1998). In this study, we introduce multiresolution Gaussian process (Fox and Dunson 2012) as a latent process governing  observed point process. This is a recursive change-point problem only from discrete observations, and we estimate it with interleaved Elliptical slice sampling of  latent Gaussian processes. We show some experiments on synthetic data, neural firing data and ecological data on forestry.