Bayesian methods have significantly enhanced the analysis of complex data structures, such as semicontinuous outcomes and zero-inflated observations, by providing coherent inferences and more ...

Bayesian Data Analysis. This information is for the 2023/24 session. Teacher responsible. Dr Sara Geneletti Inchauste Col 5.07. ... The course is a hands-on introduction and development of the ...

Slightly elevated mortality in nonagenarians highlights the need for individualized ICU care guided by more than age.

On Friday the 11th of November 2022, PhD, M.Sc. Laura Uusitalo defends her PhD thesis on Bayesian network modelling of complex systems with sparse data: Ecological case studies. The thesis is related ...

The integration of Bayesian statistics into modern analytics has redefined industries, and Alexandre Andorra’s work exemplifies its transformative potential. With expertise spanning sports ...

When estimating DSGE models, the number of observable economic variables is usually kept small, and it is conveniently assumed that DSGE model variables are perfectly measured by a single data series.

Quantification of operational risk cannot be based on historical data alone but should involve scenario analysis. Historical internal operational risk loss data has limited the ability to predict ...

By means of model-data fusion it is possible to integrate all those sources of information in forest models, ... M. Hanewinkel, and R. Yousefpour. 2017. “Productivity of Fagus Sylvatica under Climate ...

Model drift is the degradation of data analytics model performance due to changes in data and relationships between data variables. Model drift occurs when the accuracy of insights, especially ...