Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations

DOI10.1109/TSP.2012.2191966zbMath1391.62186DBLPjournals/tsp/MangionSK12WikidataQ58315078 ScholiaQ58315078MaRDI QIDQ4573828

Visakan Kadirkamanathan, Andrew Zammit-Mangion, Guido Sanguinetti

Publication date: 18 July 2018

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Bayesian inference (62F15) Signal detection and filtering (aspects of stochastic processes) (60G35) PDEs with randomness, stochastic partial differential equations (35R60)

Related Items (5)

Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework ⋮ Bayesian computation for Log-Gaussian Cox processes: a comparative analysis of methods ⋮ Deep Compositional Spatial Models ⋮ Multi-scale process modelling and distributed computation for spatial data ⋮ Estimating a Separably Markov Random Field from Binary Observations

This page was built for publication: Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations