Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Operations for learning with graphical models
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
Available knowledge to describe food processes has been capitalized from different sources, is expressed under different forms and at different scales. To reconstruct the puzzle of knowledge by taking into account uncertainty, we need to combine, integrate different kinds of knowledge. Mathematical concepts such that expert systems, neural networks or mechanistic models reach operating limits. In all cases, we are faced with the limits of available data, mathematical formalism and the limits of human reasoning. Dynamical Bayesian Networks (DBNs) are practical probabilistic graphic models to represent dynamical complex systems tainted with uncertainty. This paper presents a simplified dynamic bayesian networks which allows to represent the dynamics of microorganisms in the ripening of a soft mould cheese (Camembert type) by means of an integrative sensory indicator. The aim is the understanding and modeling of the whole network of interacting entities taking place between the different levels of the process.