This thesis introduces and evaluates ideas for the use of data augmentation in the area of Point Process Learning. Motivated by the regularizing effect of training with augmented data sets, we create a follow-up work to ...

The thesis introduces an innovative way of decreasing the computational cost of approximate Bayesian computation (ABC) simulations when implemented via Markov chain Monte Carlo (MCMC). Bayesian inference has enjoyed ...

We look at numerical methods for simulation of stochastic differential equations exhibiting volatility induced stationarity. This is a property of the process which means that the stationary behaviour is mostly imposed ...

In this thesis we investigate the behaviour of primes in arithmetic progressions, with a focus on the phenomenon known as Chebyshev’s bias. Under the assumption of the Generalized Riemann Hypothesis and the Linear ...

Due to the nancial crisis of 2007, regulatory authorities engaged in a fundamental reconsideration of how they approach nancial regulation and supervision. Therefore, that nancial crisis caused the need of a signi ...

Point process learning is a new statistical theory that gives us a way to estimate parameters using cross-validation for point processes. By thinning a point pattern we are able to create training and validation sets ...

The failure of sensors to perceive the environment correctly is one of the primary sources of risk that needs to be quantified in the development of active safety features for autonomous vehicles. By extracting training ...

Economic-based growth is increasingly becoming dependent on the wide range of intangible investments, known as knowledge-based capital (KBC) investments. Its rapid asset size growth has called for transparency in the ...

Interest in the field of spectral geometry, the study of how analytic and geometric properties of manifolds are related, was sparked when Marc Kac in 1966 asked the question “can one hear the shape of a drum?”. One of ...