Nettsider med emneord «Bayesian inference»
The objective of StREP is to provide a comprehensive and unified account of the decision to request public votes and the consequences thereof for observed behaviour.
The project consists in investigating the non-linear structure formation within the framework of dark energy, dark matter and modified gravity theories using N-body and hydrodynamic numerical simulations.
The standard model of cosmology has this last decade been increasingly challenged by observational tensions that might result eventually in the preference of some alternative model. But there is a large zoo of models to consider.
Recently maturing software for stellar modeling and evolution, such as MESA, have been applied to constraining variations of the gravitational constant and exploring such a variation’s ability to account for the Hubble tension between the late- and early- time universe by biasing the local distance ladder.
Accurate mapping of surface greenhouse gas fluxes is necessary for the validation and calibration of climate models. In this project, we develop a novel framework using drone observations and machine learning to estimate greenhouse gas fluxes at a regional scale.
LATICE aims to advance the knowledge base concerning land atmosphere interactions through improved model representation of snow, permafrost, hydrology and large-scale vegetation processes representative of high latitudes, including; 1) New ground observations (gap filling using ML); 2) Land surface model parametrization using data science methods; 3) Seasonal snow cover dynamics using data assimilation and Earth observations (e.g. satellite data, drones).
The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice/water balance for permafrost and glaciers. The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming.
Exploring the fundamental constituents of the Universe physicists are faced with very serious calculational bottlenecks. To compare new physics models to data we need to perform very computationally expensive calculations in quantum field theory (QFT).
We live in a Universe composed of a large variety of chemical elements. The element distribution we observe, and in particular the diverse abundances of atomic nuclei, tells a fascinating story of nucleosynthesis events that have taken place throughout the 13.7-billion-year-long history starting with the Big Bang.
The aim of this project is to determine the effectiveness of antidepressant treatment in pregnant and postpartum women, as well as the longer-term metabolic safety of these drugs in pregnancy on the offspring.
For maritime safety surveillance we develop new approaches
based on the availability of large arrays of sensors, which
monitor condition and performance of vessels, machinery, or
power systems.
BigInsight produces innovative solutions for key data-driven challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.
Snow in the mountains is a source for drinking water, hydropower, irrigation, but can also cause floods and geohazards. There are currently no efficient methods to measure depth of snow in mountains and remote areas.
Modern science usually provides both copious amounts of data and complicated models for the part of reality it is trying to describe. Often there is even so much data, and the models so complicated, that it becomes difficult to make full use of the data in deciding which models best describe the world around us, and finding their properties. The main goal of the GAMBIT project is to develop a software tool to help physicists do just that.
Bayesian methods have recently regained a significant amount of attention in the machine community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated.