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Scalable Inference for Markov Processes with Intractable Likelihoods
v1v2 (latest)

Scalable Inference for Markov Processes with Intractable Likelihoods

26 March 2014
J. Owen
D. Wilkinson
Colin S. Gillespie
    TPM
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Papers citing "Scalable Inference for Markov Processes with Intractable Likelihoods"

13 / 13 papers shown
Title
Particle Gibbs for Likelihood-Free Inference of State Space Models with
  Application to Stochastic Volatility
Particle Gibbs for Likelihood-Free Inference of State Space Models with Application to Stochastic Volatility
Zhaoran Hou
Samuel W.K. Wong
46
0
0
20 Dec 2023
Towards Data-Conditional Simulation for ABC Inference in Stochastic
  Differential Equations
Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations
P. Jovanovski
Andrew Golightly
Umberto Picchini
47
1
0
16 Oct 2023
Neural Continuous-Time Markov Models
Neural Continuous-Time Markov Models
Majerle Reeves
Harish S. Bhat
39
0
0
11 Dec 2022
Guided sequential ABC schemes for intractable Bayesian models
Guided sequential ABC schemes for intractable Bayesian models
Umberto Picchini
M. Tamborrino
102
8
0
24 Jun 2022
Accelerating inference for stochastic kinetic models
Accelerating inference for stochastic kinetic models
Tom Lowe
Andrew Golightly
Chris Sherlock
79
5
0
06 Jun 2022
Differentiated uniformization: A new method for inferring Markov chains
  on combinatorial state spaces including stochastic epidemic models
Differentiated uniformization: A new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models
K. Rupp
Rudolf Schill
Jonas Süskind
Peters Georg
Maren Klever
Andreas Lösch
Lars Grasedyck
T. Wettig
Rainer Spang
40
3
0
21 Dec 2021
Robust and integrative Bayesian neural networks for likelihood-free
  parameter inference
Robust and integrative Bayesian neural networks for likelihood-free parameter inference
Fredrik Wrede
Robin Eriksson
Richard M. Jiang
Linda R. Petzold
Stefan Engblom
Andreas Hellander
Prashant Singh
108
7
0
12 Feb 2021
Augmented pseudo-marginal Metropolis-Hastings for partially observed
  diffusion processes
Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes
Andrew Golightly
Chris Sherlock
65
3
0
11 Sep 2020
An automatic adaptive method to combine summary statistics in
  approximate Bayesian computation
An automatic adaptive method to combine summary statistics in approximate Bayesian computation
Jonathan U. Harrison
R. Baker
57
17
0
07 Mar 2017
Direct likelihood-based inference for discretely observed stochastic
  compartmental models of infectious disease
Direct likelihood-based inference for discretely observed stochastic compartmental models of infectious disease
L. Ho
Forrest W. Crawford
M. Suchard
59
22
0
24 Aug 2016
Birth/birth-death processes and their computable transition
  probabilities with biological applications
Birth/birth-death processes and their computable transition probabilities with biological applications
L. Ho
Jason Xu
Forrest W. Crawford
V. Minin
M. Suchard
68
41
0
11 Mar 2016
Adaptive, delayed-acceptance MCMC for targets with expensive likelihoods
Adaptive, delayed-acceptance MCMC for targets with expensive likelihoods
Chris Sherlock
Andrew Golightly
D. Henderson
109
55
0
01 Sep 2015
Likelihood free inference for Markov processes: a comparison
Likelihood free inference for Markov processes: a comparison
J. Owen
D. Wilkinson
Colin S. Gillespie
215
33
0
02 Oct 2014
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