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pro vyhledávání: '"Graham, Matthew M"'
This work aims at making a comprehensive contribution in the general area of parametric inference for discretely observed diffusion processes. Established approaches for likelihood-based estimation invoke a time-discretisation scheme for the approxim
Externí odkaz:
http://arxiv.org/abs/2211.16384
Autor:
Hallett, Timothy B *, Mangal, Tara D, Tamuri, Asif U, Arinaminpathy, Nimalan, Cambiano, Valentina, Chalkley, Martin, Collins, Joseph H, Cooper, Jonathan, Gillman, Matthew S, Giordano, Mosè, Graham, Matthew M, Graham, William, Hawryluk, Iwona, Janoušková, Eva, Jewell, Britta L, Lin, Ines Li, Manning Smith, Robert, Manthalu, Gerald, Mnjowe, Emmanuel, Mohan, Sakshi, Molaro, Margherita, Ng'ambi, Wingston, Nkhoma, Dominic, Piatek, Stefan, Revill, Paul, Rodger, Alison, Salmanidou, Dimitra, She, Bingling, Smit, Mikaela, Twea, Pakwanja D, Colbourn, Tim, Mfutso-Bengo, Joseph, Phillips, Andrew N
Publikováno v:
In The Lancet Global Health January 2025 13(1):e28-e37
Publikováno v:
In Stochastic Processes and their Applications August 2024 174
A learning procedure takes as input a dataset and performs inference for the parameters $\theta$ of a model that is assumed to have given rise to the dataset. Here we consider learning procedures whose output is a probability distribution, representi
Externí odkaz:
http://arxiv.org/abs/2012.12670
Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler divergence (KLD) from the posterior to the approximation. The KLD is a stro
Externí odkaz:
http://arxiv.org/abs/2010.11779
Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures. These pathologies naturally occur in a number of situations. For example, they are common to Bayesia
Externí odkaz:
http://arxiv.org/abs/2003.03950
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompan
Externí odkaz:
http://arxiv.org/abs/1912.02982
Filtering in spatially-extended dynamical systems is a challenging problem with significant practical applications such as numerical weather prediction. Particle filters allow asymptotically consistent inference but require infeasibly large ensemble
Externí odkaz:
http://arxiv.org/abs/1906.00507
Autor:
Giles, Daniel1,2 (AUTHOR) d.giles@ucl.ac.uk, Graham, Matthew M.1 (AUTHOR), Giordano, Mosè1 (AUTHOR), Koskela, Tuomas1 (AUTHOR), Beskos, Alexandros2,3 (AUTHOR), Guillas, Serge1,2,3 (AUTHOR)
Publikováno v:
Geoscientific Model Development. 2024, Vol. 17 Issue 6, p2427-2445. 19p.
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