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Visar resultat 1 - 5 av 158 avhandlingar som matchar ovanstående sökkriterier.
1. Scalable Bayesian spatial analysis with Gaussian Markov random fields
Sammanfattning : Accurate statistical analysis of spatial data is important in many applications. Failing to properly account for spatial autocorrelation may often lead to false conclusions. LÄS MER
2. Spatial sampling and prediction
Sammanfattning : This thesis discusses two aspects of spatial statistics: sampling and prediction. In spatial statistics, we observe some phenomena in space. Space is typically of two or three dimensions, but can be of higher dimension. LÄS MER
3. Computational Methods for Image-Based Spatial Transcriptomics
Sammanfattning : Why does cancer develop, spread, grow, and lead to mortality? To answer these questions, one must study the fundamental building blocks of all living organisms — cells. Like a well-calibrated manufacturing unit, cells follow precise instructions by gene expression to initiate the synthesis of proteins, the workforces that drive all living biochemical processes. LÄS MER
4. On flexible random field models for spatial statistics: Spatial mixture models and deformed SPDE models
Sammanfattning : Spatial random fields are one of the key concepts in statistical analysis of spatial data. The random field explains the spatial dependency and serves the purpose of regularizing interpolation of measured values or to act as an explanatory model. LÄS MER
5. Satistical Modelling Of CO2 Exchange Between Land And Atmosphere : Using Stochastic Optimisation And Gaussian Markov Random Fields
Sammanfattning : This thesis focuses on the development and application of efficient mathematicaltools for estimating and modelling the exchange of carbon dioxide (CO2) between the Earth and its atmosphere; here referred to as the global CO2 surface flux.There are two main approaches for estimating the CO2 flux: Processed based(bottom-up) modelling and atmospheric inversion (top-down) modelling. LÄS MER