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Authored
4 records found
Decompounding discrete distributions
A nonparametric Bayesian approach
Suppose that a compound Poisson process is observed discretely in time and assume that its jump distribution is supported on the set of natural numbers. In this paper we propose a nonparametric Bayesian approach to estimate the intensity of the underlying Poisson process and the
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A Bayesian stochastic generator to complement existing climate change scenarios
Supporting uncertainty quantification in marine and coastal ecosystems
Available climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low- (RCP2.6), moderate- (RCP4.5) or high clima
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We consider the current status continuous mark model where, if an event takes place before an inspection time T a “continuous mark” variable is observed as well. A Bayesian nonparametric method is introduced for estimating the distribution function of the joint distribution of ...
A continuous-time Markov process X can be conditioned to be in a given state at a fixed time T>0 using Doob's h-transform. This transform requires the typically intractable transition density of X. The effect of the h-transform can be described as introducing a guiding forc ...
Contributed
16 records found
A Spatial Markov Chain Cellular Automata Model for the Spread of the COVID-19 virus
Including parameter estimation
In this bachelor thesis we propose a spatial Markov Chain Cellular Automata
model for the spread of the COVID-19 virus as well as two methods for parameter
estimation. Network topologies are used to model the progression of the epidemic
by considering each individual ...
model for the spread of the COVID-19 virus as well as two methods for parameter
estimation. Network topologies are used to model the progression of the epidemic
by considering each individual ...
Likelihood-based Inference on Nonlinear spaces
Using Diffusion Processes on Riemannian Manifolds
When data in higher dimensions with a certain constraint on it, say a set of locations on a sphere, is encountered, some classical statistical analysis methods fail, as the data no longer assumes its values in a linear space. In this thesis we consider such datasets and aim to do
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Efficient stochastic simulation on discrete spaces
Using balancing functions to incorporate local target density information into Markov Chain Monte Carlo sampling schemes
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-Hastin
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Efficient Inference with Panel Data
On the pass-through of the Dutch 2001 and 2012 VAT increases to consumer prices
This thesis evaluates the pass-through of the 2001 and 2012 Dutch Value Added Tax (VAT) increases to customer prices using a difference-in-differences model. To this end, the first difference and feasible generalised least squares estimators are introduced. Contrary to the conven
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Loads of lifestyles
A latent lifestyle model for interpreting and simulating electrical energy consumption
The rapid and uncertain penetration of distributed energy resources is changing the way people are consuming electricity. This development increases the risk of instability and congestion in the grid, posing great challenges to system operators in the near future. In order to opt
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In anti-cancer therapy, ntiangiogenic treatments are applied and take effect on the vascularization of tissue. To evaluate the efficacy of treatments, we adopt two methods to solve the physiological pharmacokinetic model’s parameter estimation problem, providing discrete, partial
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Dit project vergelijkt drie verschillende kernels (Random Walk, Langevin en Barker) die gebruikt kunnen worden in het Metropolis-Hastings algoritme aan de hand van voorbeelden.
The development of computationally ecient algorithms for statistical inference of stochastic dierential equations has been a long-standing research subject ever since the advent of stochastic analysis. Recently, there has been an increasing interest in extending these methods to
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Free competition in the insurance markets increases the competitiveness and lowers the premiums. If insurers lower their premiums without having a model that accurately quantifies the expected claim size, they can be in serious trouble. This research aims to accurately model the
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The derivation of water quality indicators is of importance, especially in coastal areas, as most of the economic activities are located here. However, the availability of high-spatial-resolution water quality information in coastal zones is limited. Nowadays, high-resolution sat
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In research there is often a need to choose between multiple competing models. Two popular criteria for model selection are the AIC and BIC. The AIC excels in estimating the best model for the unknown data generating process. The BIC on the other hand is consistent in finding the
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The goal of this thesis is to estimate parameters in a bidimensional Ornstein-Uhlenbeck process, namely a diffusion model which can be found in Favetto and Samson (2010), which considers plasma and interstitium concentrations. We first look at a general linear stochastic differen
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In many fields we are interested in inference for a complex stochastic process given limited observations regarding its state over time. This thesis therefore introduces an expectation propagation approach to backward filtering forward guiding for high-dimensional finite-state sp
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Burn injuries occur daily and can have severe physical and mental effects both in short and long term, such as disabilities due to severe skin contraction. Even though the mortality rate has decreased over the years, the need for a higher quality of life after severe burns remain
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Dropout is one of the most popular regularization methods used in deep learning. The general form of dropout is to add random noise to the training process, limiting the complexity of the models and preventing overfitting. Evidence has shown that dropout can effectively reduce ov
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