Variational Inference for Gaussian Process Modulated Poisson Processes
We present the first fully variational Bayesian inference scheme for continuous Gaussian-process-modulated Poisson processes. Such point processes are used in a variety of domains, including neuroscience, geo-statistics and astronomy, but their use is hindered by the computational cost of existing inference schemes. Our scheme: requires no discretisation of the domain; scales linearly in the number of observed events; and is many orders of magnitude faster than previous sampling based approaches. The resulting algorithm is shown to outperform standard methods on synthetic examples, coal mining disaster data and in the prediction of Malaria incidences in Kenya.
Code (0)
등록된 구현이 없습니다.
Tasks
AstronomyBayesian InferencePoint ProcessesVariational InferenceSimilar Papers 제목 키워드 기반
Variational Inference for Gaussian Process with Panel Count Data
We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts. Panel count data frequently arise when experimental subjects are observed only at…
Variational InferenceMulti-output Gaussian Process Modulated Poisson Processes for Event Prediction
Prediction of events such as part replacement and failure events plays a critical role in reliability engineering. Event stream data are commonly observed in manufacturing and teleservice systems. Designing predictive mo…
PredictionVariational InferenceLarge-Scale Cox Process Inference using Variational Fourier Features
Gaussian process modulated Poisson processes provide a flexible framework for modelling spatiotemporal point patterns. So far this had been restricted to one dimension, binning to a pre-determined grid, or small data set…
GPUSmall Data Image ClassificationGaussian process modulated renewal processes
Renewal processes are generalizations of the Poisson process on the real line, whose intervals are drawn i.i.d. from some distribution. Modulated renewal processes allow these distributions to vary with time, allowing th…
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
The Cox process is a flexible event model that can account for uncertainty of the intensity function in the Poisson process. However, previous approaches make strong assumptions in terms of time stationarity, potent…
Variational Inference