Projects
New insights into strategic HRM: a configurational and Bayesian network approach to HR practices, organization of work and leadership KU Leuven
Research in Strategic HRM has mainly focused on the relationship between HR practices and firm performance. In this project, we argue that this focus is one-sided and too simplistic. First, next to HR practices, the organization of work and leadership should be considered, as they also influence employees' behavior and therefore drive firm performance. Second, in an era of sustainable and employee-centred HRM, research should not only focus ...
Bayesian methods for the inclusion of historical data in Phase I and Phase II clinical studies Hasselt University
Multilevel Markov Chain Monte Carlo methods for Bayesian full-field data assimilation KU Leuven
Markov chain Monte Carlo (MCMC) is an indispensable tool in sampling probability distributions for which a direct sampling is impossible, including Bayesian data assimilation. For data assimilation, Markov chain Monte Carlo methods are used to sample the posterior probability distribution for a parameter that needs to be estimated, given a prior distribution for the parameter and a model that allows computing the likelihood of the data for a ...
Computational methods for infinite-dimensional Bayesian inversion of physics-based models in engineering applications KU Leuven
Modeling Dose-Response Microarray Data Using Bayesian Variable Selection (BVS) Methods Hasselt University
Isntitutional and spatial analysis of water sharing mechanism of a river basin in India Ghent University
Objectives are to analyze existing water governance structure, political choice of sharing mechanism and the spatial distribution pattern of irrigation water, valuate property rights and to devleop a Spatio-Temporal model for water sharing for efficient sharing. Here water institutions, space-time interactions and water pricing are used in modeling of water sharing (bayesian modeling) and political economy models for water governance.
Design and analysis of choice experiments involving mixtures KU Leuven
Many products and services involve mixtures of ingredients, where the mixtures can be expressed as combinations of ingredient proportions, for instance, flour, water and yeast to make bread. In many cases, the result of the mixture may also depend on the way in which the mixtures are processed, such as the baking time and baking temperature for a bread. These types of variables are generally called process variables.
In mixture ...
Development and analysis of spatially explicit models of biological processes. Ghent University
Even though spatially explicit models have become more widely appreciated, there is still no framework that allows for their systematic analysis, calibration and validation. Hence, the main objective of this project is to establish such a framework starting from the first steps already taken in this direction and involving equation-free methods, approximate Bayesian computation and dynamical system measures.