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Rhythmic, periodic processes are ubiquitous in biological systems; for example, the heart beat, walking, circadian rhythms and the menstrual cycle. Modeling these processes with high delity as peri...
This paper presents work on parameter estimation methods for bursting neural models. In our approach we use both geometrical features specific to bursting, as well as general features such as perio...
We demonstrate the generalizability of the Extended Baum-Welch (EBW) algorithm not only for HMM parameter estimation but for decoding as well. We show that there can exist a general function associate...
We investigate the FFT (Fast Fourier Transform) model and G-CSF (granulocytecolony-stimulating factor) treatment of CN (Cyclical Neutropenia). We collect grey collies and normal dog!ˉs data from CN an...
Abstract: This work propose a sequential procedure which is useful to select the best model among several nested non-linear models and to estimate efficiently the parameters of the chosen model. At ea...
Approximating joint smoothing distributions using particle-based methods is a well-known issue in statistical inference when operating on general state space hidden Markov models (HMM). In this paper ...
We study multipath parameter estimation from orthogonal frequency division multiplex signals transmitted over doubly dispersive mobile radio channels. We are interested in cases where the transmissio...
The paper considers gossip distributed estimation of a (static) distributed random field (a.k.a., large scale unknown parameter vector) observed by sparsely interconnected sensors, each of which only ...
Transferring information from observations of a dynamical system to estimate the fixed parameters and unobserved states of a system model can be formulated as the evaluation of a discrete time path in...
In this paper, we obtain Bayesian and non-Bayesian estimators for the parameter of the Burr type X distribution under grouped data. We apply our results to artificial data.
In this paper we develop a new upper bound for the mean square estimation error of a parameter that takes values on a bounded interval. The bound is based on the discretization of the region into a fi...

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