Scalable Bayesian Learning for State Space Models using Variational
Inference with SMC Samplers

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Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers

23 May 2018

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Papers citing "Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers"

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Title
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport Adrien Corenflos James Thornton George Deligiannidis Arnaud Doucet OT 91 70 0 15 Feb 2021
Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization Nicola Branchini Victor Elvira 84 19 0 18 Nov 2020
Variational Dynamic Mixtures Chen Qiu Stephan Mandt Maja R. Rudolph BDL AI4TS 62 2 0 20 Oct 2020
Fast and Flexible Temporal Point Processes with Triangular Maps Oleksandr Shchur Nicholas Gao Marin Bilovs Stephan Günnemann 115 35 0 22 Jun 2020
Relational State-Space Model for Stochastic Multi-Object Systems Fan Yang Ling Chen Fan Zhou Yusong Gao Wei Cao 75 8 0 13 Jan 2020
The Neural Moving Average Model for Scalable Variational Inference of State Space Models Tom Ryder D. Prangle Andrew Golightly Isaac Matthews BDL AI4TS 83 6 0 02 Oct 2019
On importance-weighted autoencoders Axel Finke Alexandre Hoang Thiery 15 2 0 24 Jul 2019