The performance of monocular vision based target tracking is a strong function of camera motion. Without motion, the target estimation problem is unsolvable. By designing the camera path, the best possible estimator performance can be achieved. This paper describes a trajectory design method based on the predicted target state error covariance. This method uses a pyramid, breadth-first search a...

The standard Tobit maximum likelihood estimator under zero censoring threshold produces inconsistent parameter estimates, when the constant censoring threshold γ is nonzero and unknown. Unfortunately, the recording of a zero rather than the actual censoring threshold value is typical of economic data. Non-trivial minimum purchase prices for most goods, fixed cost for doing business or trading, ...

In this paper, an uncertainty and disturbance estimator (UDE)-based control is employed to ensure the finite-time tracking rejection performance for a class of Takagi–Sugeno fuzzy switched systems including additive time-varying delays, unknown uncertainties disturbances. To be precise, robust achieved by estimating disturbances with aid low-pass filter means appropriate bandwidth choice desire...

Cristian M. Radu Daniel T. Kaplan Department of Physiology and Centre of Nonlinear Dynamics in Physiology and Medicine McGill University, 3655 Drummond, Montr eal H3G-1Y6, Qu ebec, Canada Abstract This paper outlines our design of a continuous estimator for the sympathetic innervation of the heart. The estimator is computed by linear methods, yet it is tested on a nonlinear, detailed model of c...

The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper we describe a way to use posterior simulation output to estimate marginal likelihoods. We describe the basic Laplace-Metropolis estimator for models without random eeects. For models wi...

In this paper I estimate empirical growth models simultaneously considering endogenous regressors and model uncertainty. In order to apply Bayesian methods such as Bayesian Model Averaging (BMA) to dynamic panel data models with predetermined or endogenous variables and fixed effects, I propose a likelihood function for such models. The resulting maximum likelihood estimator can be interpreted ...

-Procedures to estimate missing data, determine extrema, and derive uncertainties for data collected in ambient air monitoring networks are presented. The optimal linear estimators used obtain unbiased, minimum variance results based on the temporal and spatial correlation of the data and estimates of sample uncertainty. The first estimator interpolates missing data. The second estimator derive...

A state estimator is an algorithm that computes the current state of a time-varying system from on-line measurements. Physical quantities such as measurements and parameters are characterised by uncertainty. Understanding how uncertainty affects the accuracy of state estimates is therefore a prerequisite to the application of such techniques to real systems. In this paper we develop a method of...

Hall’s recent derivation of an exact uncertainty relation [Phys. Rev. A64, 052103 (2001)] is revisited. It is found that the Bayes estimator of an observable between preand postselection equals the real part of the weak value. The quadratic loss function equals the expectation of the squared imaginary part of the weak value. Hall recently solved the problem of finding the most efficient estimat...