between error estimation and data-based complexity penalization: any good error estimate may be converted into a data …

motion (GBM) process. That is, we show what difficulties can arise when failing to account for estimation risk. Our working … parameter sigma, we derive the resulting marginal distribution of the approximated GBM. This allows us to derive post-estimation … results for the technically more-complicated formulae associated with our post-estimation process. We discuss an assortment of …

estimation error and a communication cost. In our formulation, the transmission of a real number from the pre-processor to the …

stable estimates. Furthermore, convergence can be exceedingly slow, making estimation computationally expensive and multiple … random restarts doubly so. We derive an expectation maximization algorithm for maximum likelihood estimation mutually … exciting processes that is faster, more robust, and less biased than estimation based on numerical optimization. For an …

-called endmembers and fractional values in a linear mixing model. We describe one of the estimation methods based on MVSEP. We show … numerically that using nonlinear optimization local search leads to the estimation results aimed at. This is done using examples …