Sammanfattning
Gaussian processes are certainly not a new tool in the field of science. However, alongside the quick increasing of computer power during the last decades, Gaussian processes have proved to be a successful and flexible statistical tool for data analysis. Its practical interpretation as a nonparametric procedure to represent prior beliefs about the underlying data generating mechanism has gained attention among a variety of research fields ranging from ecology, inverse problems and deep learning in artificial intelligence.
The core of this thesis deals with multivariate Gaussian process model as an alternative method to classical methods of regression analysis in Statistics. I develop hierarchical models, where the vector of predictor functions (in the sense of generalized linear models) is assumed to follow a multivariate Gaussian process. Statistical inference over the vector of predictor functions is approached by means of the Bayesian paradigm with analytical approximations.
I developed also new parametrisations for the statistical models in order to improve the performance of the computations related to the inferential task. The methods developed in this thesis are also tightly connected to practical applications. The main applications considered involve multiple species surveys and species distribution modelling in quantitative ecology. This is a field of research which provides a rich variety of applications where statistical methods can be put at test.
The core of this thesis deals with multivariate Gaussian process model as an alternative method to classical methods of regression analysis in Statistics. I develop hierarchical models, where the vector of predictor functions (in the sense of generalized linear models) is assumed to follow a multivariate Gaussian process. Statistical inference over the vector of predictor functions is approached by means of the Bayesian paradigm with analytical approximations.
I developed also new parametrisations for the statistical models in order to improve the performance of the computations related to the inferential task. The methods developed in this thesis are also tightly connected to practical applications. The main applications considered involve multiple species surveys and species distribution modelling in quantitative ecology. This is a field of research which provides a rich variety of applications where statistical methods can be put at test.
Originalspråk  engelska 

Tilldelande institution 

Tilldelningsdatum  20 mar 2019 
Utgivningsort  University of Helsinki 
Utgåva  1st 
Förlag  
Tryckta ISBN  789515149749 
Elektroniska ISBN  9789515149756 
Status  Publicerad  20 mar 2019 
MoEpublikationstyp  G5 Doktorsavhandling (artikel) 
Vetenskapsgrenar
 111 Matematik
 112 Statistik
 113 Data och informationsvetenskap
 1172 Miljövetenskap