By Prof. Dr. Jesús Portilla. Escuela Politécnica Nacional
Fecha seminario: 2022-05-19
Management of environmental data requires synthetization this is generally achived by numerical or mathematical thecniques such as clustering, neural networks, or principal components analysis. However, for large and complex data, and in absence of "constraints" these methods hardly converge to a coherent output, or sometimes, if they do have the skill to converge, the computational burden is overwhelming. In order to get around that limitation, we introduce prior information of the pysical system in order to constraint the data (physically or statistically), and reach a solution. The coherence (used now loosely for convergence) is verified by the physical consistency of the result, and the computations are much cheaper. However, although we remain in a Bayesian framework, the mathematical purity of the method is compromised.