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 ...

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 ...

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 ...

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 ...

Spatio-temporal analysis of COVID-19 data with the two different statistical approaches is the main objective of this thesis. The first classical approach, the Endemic-Epidemic framework (Held et al., 2005) is a class of ...

Chronic pain is a cause of suffering in a large share of the population and a leading public health problem. Multimodal pain rehabilitation (MMR) is a multidisciplinary rehabilitation method commonly used to treat chronic ...

We study the low-lying zeros of Artin L-functions associated to non-Galois cubic number fields through their one- and two-level densities. In particular, we find new precise estimates for the two-level density with a ...