Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Syed, Saifuddin"'
Annealed Sequential Monte Carlo (SMC) samplers are special cases of SMC samplers where the sequence of distributions can be embedded in a smooth path of distributions. Using this underlying path of distributions and a performance model based on the v
Externí odkaz:
http://arxiv.org/abs/2408.12057
Non-reversible parallel tempering (NRPT) is an effective algorithm for sampling from target distributions with complex geometry, such as those arising from posterior distributions of weakly identifiable and high-dimensional Bayesian models. In this w
Externí odkaz:
http://arxiv.org/abs/2405.11384
Autor:
Biron-Lattes, Miguel, Surjanovic, Nikola, Syed, Saifuddin, Campbell, Trevor, Bouchard-Côté, Alexandre
Selecting the step size for the Metropolis-adjusted Langevin algorithm (MALA) is necessary in order to obtain satisfactory performance. However, finding an adequate step size for an arbitrary target distribution can be a difficult task and even the b
Externí odkaz:
http://arxiv.org/abs/2310.16782
Autor:
Surjanovic, Nikola, Biron-Lattes, Miguel, Tiede, Paul, Syed, Saifuddin, Campbell, Trevor, Bouchard-Côté, Alexandre
We introduce a software package, Pigeons.jl, that provides a way to leverage distributed computation to obtain samples from complicated probability distributions, such as multimodal posteriors arising in Bayesian inference and high-dimensional distri
Externí odkaz:
http://arxiv.org/abs/2308.09769
Autor:
Williams, Christopher, Falck, Fabian, Deligiannidis, George, Holmes, Chris, Doucet, Arnaud, Syed, Saifuddin
U-Nets are a go-to, state-of-the-art neural architecture across numerous tasks for continuous signals on a square such as images and Partial Differential Equations (PDE), however their design and architecture is understudied. In this paper, we provid
Externí odkaz:
http://arxiv.org/abs/2305.19638
Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on the expanded state space of a sequence of distributions interpolating
Externí odkaz:
http://arxiv.org/abs/2206.00080
Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms that constructs a path of distributions annealing between a tractable reference and an intractable target, and then interchanges states along the path to improve mixing in the
Externí odkaz:
http://arxiv.org/abs/2102.07720
Publikováno v:
Journal of Ophthalmic & Vision Research, Vol 18, Iss 2, Pp 240-244 (2023)
Abstract Purpose: To report a case of a rare disease entity Posterior Microphthalmos Pigmentary Retinopathy Syndrome (PMPRS) in a 47-year-old female with a brief review of literature. Case Report: A 47-year-old woman presented with a history of defec
Externí odkaz:
https://doaj.org/article/7460a253a1984da0a14fa2f222094f13
Exchangeability -- in which the distribution of an infinite sequence is invariant to reorderings of its elements -- implies the existence of a simple conditional independence structure that may be leveraged in the design of statistical models and inf
Externí odkaz:
http://arxiv.org/abs/1906.09507
Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes used to sample complex high-dimensional probability distributions. They rely on a collection of $N$ interacting auxiliary chains targeting tempered versions of th
Externí odkaz:
http://arxiv.org/abs/1905.02939