We present an approximate Bayesian inference approach for estimating the intensity of a inhomogeneous Poisson process, where the intensity function is modelled ...

Abstract. We present an approximate Bayesian inference approach for estimating the intensity of a inhomogeneous Poisson process, where the intensity ...

Abstract. We present an approximate Bayesian inference approach for estimating the intensity of a inhomogeneous Poisson process, where the intensity ...

We present an approximate Bayesian inference approach for estimating the intensity of a inhomogeneous Poisson process, where the intensity function is ...

Dec 17, 2018 · We present an approximate Bayesian inference approach for estimating the intensity of an inhomogeneous Poisson process, where the intensity ...

Donner, C., & Opper, M. (2018). Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. Journal of Machine Learning Research, 19(67). Link to ...

In this thesis we present a variety of new, continuous, Bayesian Gaussian-process-driven. Cox process models. These are used to model sparse event data ...

We propose an efficient algorithm to solve the integral equations; it offers O(NL + L2) computation scaling, that is, lower complexity than the state-of-the-art ...

This paper considers the sigmoid Gaussian Hawkes process model, and derives an expectation-maximization algorithm to obtain the maximum a posteriori (MAP) ...

Nov 23, 2023 · We propose a BO framework based on the Gaussian Cox process model and further develop a Nyström approximation for efficient computation.