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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(More)
Modern applications of data fusion are rarely starved of data but they look more challenging because the data can be diverse (hard and soft), uncertain and ambiguous, and often swamped by irrelevant detail. This paper presents a mathematical framework for dealing with these issues. It manages diverse data by representing it in the common format of a kernel(More)
This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel contribution enables the model to work(More)
We introduce a probabilistic model for the factorisation of continuous Poisson process rate functions. Our model can be thought of as a topic model for Poisson point processes in which each point is assigned to one of a set of latent rate functions that are shared across multiple outputs. We show that the model brings a means of incorporating structure in(More)
Information fusion algorithms for data association and inference are applied to a representative intelligence gathering problem in which signals of intent are monitored by multiple imperfect sensors over a period of time. Two sets of algorithms are developed: a brute force set which makes best use of the data but is not efficient, and an approximate set(More)
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