: An example was given in the textbook All of Statistics (Wasserman, 2004, pages 186-188) for arguing that, in the problems with a great many parameters Bayesian inferences are wea...
We consider a network of sensors deployed to sense a spatio-temporal field and infer parameters of interest about the field. We are interested in the case where each sensor's...
S. Sundhar Ram, Venugopal V. Veeravalli, Angelia N...
Linear inverse problems are very common in signal and image processing. Many algorithms that aim at solving such problems include unknown parameters that need tuning. In this work...
Background: Tiling arrays are an important tool for the study of transcriptional activity, proteinDNA interactions and chromatin structure on a genome-wide scale at high resolutio...
Background: Simulating the major molecular events inside an Escherichia coli cell can lead to a very large number of reactions that compose its overall behaviour. Not only should ...
Marco A. J. Iafolla, Guang Qiang Dong, David R. Mc...
Background: Current approaches to parameter estimation are often inappropriate or inconvenient for the modelling of complex biological systems. For systems described by nonlinear ...
Background: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractab...
Daniel Wegmann, Christoph Leuenberger, Samuel Neue...
Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H2 performance crit...
Alexandre S. Bazanella, Michel Gevers, Ljubisa Mis...
The accuracy of plant parameters estimated in closed-loop operation is investigated for a class of multivariable systems and for the situation where only some of the reference inp...
Linear System Identification yields a nominal model parameter, which minimizes a specific criterion based on the single inputoutput data set. Here we investigate the utility of va...