Searched for: subject%3A%22Markov%255C%2Bchain%255C%2BMonte%255C%2BCarlo%22
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Bierkens, G.N.J.C. (author), Kamatani, Kengo (author), Roberts, Gareth O. (author)
Piecewise deterministic Markov processes are an important new tool in the design of Markov chain Monte Carlo algorithms. Two examples of fundamental importance are the bouncy particle sampler (BPS) and the zig–zag process (ZZ). In this paper scaling limits for both algorithms are determined. Here the dimensionality of the space tends towards...
journal article 2022
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Mignacco, Chiara (author)
Since their introduction in 1993, particle filters are amongst the most popular algorithms for performing Bayesian inference on state space models that do not admit an analytical solution. In this thesis, we will present several particle filtering algorithms adapted to a class of models known as Piecewise Deterministic Markov Processes (PDMP), i...
master thesis 2022
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van Dijk, Raymond (author)
The microscope is an essential tool for biologists. Since the late 16th century, it has given researchers a better understanding of cell processes and greatly advanced healthcare. In this century, Single molecule localization microscopy (SMLM) has revolutionized optical microscopy by breaking the optical diffraction limit. Sparsely activating...
master thesis 2022
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Herben, Bjarne (author)
The breadth of theoretical results on efficient Markov Chain Monte Carlo (MCMC) sampling schemes on discrete spaces is slim when compared to the available theory for MCMC sampling schemes on continuous spaces. Nonetheless, in [Zan17] a simple framework to design Metropolis-Hastings (MH) proposal kernels that incorporate local information about...
bachelor thesis 2022
document
Bergen, Willemijn (author)
The spread of Covid-19 is modelled in this Bachelor thesis. This is done using two compartmental epidemiological models: the SIR-model and SIRS-model. These models divide the population into three groups, namely Susceptible, Infected and Recovered. Artificial data is generated based on the stochastic version of the models. The models are applied...
bachelor thesis 2022
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Schaap, Jop (author)
The superoptimizer STOKE has previously been shown to be effective at optimizing programs containing floating-point numbers. The STOKE optimizer obtains these results by running a stochastic search over the set of all programs and selecting the best-optimized one. This study aims to find more clearly what floating-point programs STOKE optimizes...
bachelor thesis 2022
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Lye, Adolphus (author), Cicirello, A. (author), Patelli, Edoardo (author)
Bayesian inference is a popular approach towards parameter identification in engineering problems. Such technique would involve iterative sampling methods which are often robust. However, these sampling methods often require significant computational resources and also the tuning of a large number of parameters. This motivates the development...
journal article 2022
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Mider, Marcin (author), Schauer, Moritz (author), van der Meulen, F.H. (author)
Suppose X is a multivariate diffusion process that is observed discretely in time. At each observation time, a transformation of the state of the process is observed with noise. The smoothing problem consists of recovering the path of the process, consistent with the observations. We derive a novel Markov Chain Monte Carlo algorithm to sample...
journal article 2021
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Rahimi Dalkhani, A. (author), Zhang, Xin (author), Weemstra, C. (author)
Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been exploited. Conventionally, two...
journal article 2021
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Wiessner, Manfred (author), Angerer, Paul (author), van der Zwaag, S. (author), Gamsjäger, Ernst (author)
We describe the analysis of in-situ HT-XRD data of a dual phase stainless steel exposed to a complex thermal cycle of heating, holding and cooling. For the conditions used only low quality diffraction data could be collected. Peak positions, peak areas and peak broadening are modeled by the Rietveld method. The low signal-to noise ratio and...
journal article 2021
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Schoups, G.H.W. (author), Nasseri, M. (author)
To fully benefit from remotely sensed observations of the terrestrial water cycle, bias and random errors in these data sets need to be quantified. This paper presents a Bayesian hierarchical model that fuses monthly water balance data and estimates the corresponding data errors and error-corrected water balance components (precipitation,...
journal article 2021
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Andrieu, Christophe (author), Dobson, P. (author), Wang, Andi Q. (author)
We extend the hypocoercivity framework for piecewise-deterministic Markov process (PDMP) Monte Carlo established in [2] to heavy-tailed target distributions, which exhibit subgeometric rates of convergence to equilibrium. We make use of weak Poincaré inequalities, as developed in the work of [15], the ideas of which we adapt to the PDMPs of...
journal article 2021
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Lye, Adolphus (author), Cicirello, A. (author), Patelli, Edoardo (author)
This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the context of Bayesian model updating for engineering applications. Markov Chain Monte Carlo, Transitional Markov Chain Monte Carlo, and Sequential Monte Carlo methods are introduced, applied to different case studies and finally their performance is compared....
journal article 2021
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Gangapersad, Ravish (author)
In this report, our goal is to find a way to get some information such as the mean out of high dimensional densities. If we want to calculate the mean we need to calculate integrals, which are difficult to do for high dimensional densities. We cannot use the analytical or classical (deterministic) numerical rules for high dimensional problems...
bachelor thesis 2020
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Lunderman, Spencer (author), Morzfeld, Matthias (author), Glassmeier, F. (author), Feingold, Graham (author)
Predator–prey dynamics have been suggested as simplified models of stratocumulus clouds, with rain acting as a predator of the clouds. We describe a mathematical and computational framework for estimating the parameters of a simplified model from a large eddy simulation (LES). In our method, we extract cycles of cloud growth and decay from...
journal article 2020
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Schürmann, Femke (author)
This thesis discusses and compares methods which try to approximate the assymptotic variance.
bachelor thesis 2019
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Butler, Jacob (author)
The project concerns uncertainty reduction of parameters of a thermodynamic equation of state for a dense gas, using Bayesian inference. The dense gas considered is D6 siloxane and the equation of state used is the polytropic van der Waals equation. The shock tube data comes from the flexible asymmetric shock tube (FAST) experiment. This is...
master thesis 2018
document
Mavritsakis, Antonios (author)
Insufficient information on soil parameters and their spatial variability pose as a main factors of uncertainty in geotechnical design. When soil response differs from the expected, the notion of inverse analysis becomes relevant; back-calculating the parameter set able to reproduce the monitored observations. Accordingly, its application...
master thesis 2017
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Bierkens, G.N.J.C. (author), Roberts, Gareth (author)
In Turitsyn, Chertkov and Vucelja [Phys. D 240 (2011) 410-414] a nonreversible Markov Chain Monte Carlo (MCMC) method on an augmented state space was introduced, here referred to as Lifted Metropolis-Hastings (LMH). A scaling limit of the magnetization process in the Curie-Weiss model is derived for LMH, as well as for Metropolis-Hastings (MH...
journal article 2017
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Thijssen, B. (author), Dijkstra, Tjeerd M.H. (author), Heskes, Tom (author), Wessels, L.F.A. (author)
Background<br/>Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and...
journal article 2016
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