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Fast MCMC sampling for Markov jump processes and extensions
TLDR
In this paper, we tackle the problem of simulating from the posterior distribution over paths in these models, given partial and noisy observations; this typically cannot be performed analytically. Expand
Relational Pooling for Graph Representations
TLDR
This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Expand
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
TLDR
We introduce a kernelized Stein discrepancy measure for discrete spaces, and develop a nonparametric goodness-of-fit test for discrete distributions with intractable normalization constants. Expand
Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
TLDR
We consider a simple and overarching representation for permutation-invariant functions of sequences (or multiset functions). Expand
Spatial Normalized Gamma Processes
TLDR
We propose a simple and general framework to construct dependent DPs by marginalizing and normalizing a single gamma process over an extended space such that neighbouring DPs are more dependent. Expand
Gaussian process modulated renewal processes
TLDR
In this paper, we describe a nonparametric Bayesian approach where a renewal process is modulated by a random intensity function which is given a Gaussian process prior. Expand
A Multitask Point Process Predictive Model
TLDR
We propose a multitask point process model, leveraging information from all tasks via a hierarchical Gaussian process (GP). Expand
Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
TLDR
We propose a novel Markov chain Monte Carlo sampling method for Markov jump processes and continuous-time Bayesian networks that avoids the need for such expensive computations. Expand
Dependent Normalized Random Measures
TLDR
In this paper we propose two constructions of dependent normalized random measures, a class of nonparametric priors over dependent probability measures. Expand
MCMC for continuous-time discrete-state systems
TLDR
We propose a simple and novel framework for MCMC inference in continuous-time discrete-state systems with pure jump trajectories. Expand
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