In the report the approach to estimation of quality of planned experiments is considered. This approach is based on the analysis of uncertainty, which will take place under the future hypotheses testing about the existence of a new phenomenon in Nature. The probability of making a correct decision in hypotheses testing is proposed as estimator of quality of planned experiments. This estimator a...

Weak gravitational lensing by an intervening large-scale structure induces a distinct signature in the cosmic microwave background ~CMB! that can be used to reconstruct the weak-lensing displacement map. Estimators for individual Fourier modes of this map can be combined to produce an estimator for the lensing-potential power spectrum. The naive estimator for this quantity will be biased upward...

This paper focuses on optimal estimators of the magnitude spectrum for speech enhancement. We present an analytical solution for estimating in the MMSE sense the magnitude spectrum when the clean speech DFT coefficients are modeled by a Laplacian distribution and the noise DFT coefficients are modeled by a Gaussian distribution. Furthermore, we derive the MMSE estimator under speech presence un...

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. vVe describe the basic LaplaceMetropolis estimator for models without random effects. For models w...

We derive the limiting distribution of the Oaxaca estimator of average treatment effects studied by Kline (2011). A consistent estimator of the asymptotic variance is proposed that makes use of standard regression routines. It is shown that ignoring uncertainty in group means will tend to lead to an overstatement of the asymptotic standard errors. Monte Carlo experiments examine the finite samp...

Record linkage, or exact matching, can be used to join together two files that contain information on the same individuals, but lack unique personal identification codes. The possibility of errors in linkage causes problems for estimating the relationships between variables on the two files. The effect is analogous to the impact of measurement error. A model of a linear regression relationship ...

Modelling and reducing uncertainty are two essential problems with mobile robot localisation. In this paper, a new robot position estimator, the Gaussian mixture of Bayes (GMB) which utilises a density estimation technique, is introduced in particular. The proposed system, namely the GMB robot position estimator, which allows a robot's position to be modelled as a probability distribution, and ...

In the current competitive, integrated approach to supply chain management and distribution network design, especially in conditions of uncertainty, in order to ensure timely needs of customers in terms of savings in costs and raise the level of customer service, in recent years. Estimating the stochastic demand for transportation distribution networks is a crucial factor in transport planning ...

This paper presents a new approach to the design of a robust observer-based fault detection scheme for diagnosing incipient faults, called H∞ robust fault detection observer (RFDO). It takes into account the robustness of the fault detection observer against disturbances and sensitivity to faults simultaneously. The approach has originated from the robust H∞ estimator which minimizes the effect...

The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration-uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calibration, but its covariance structure. The idea of the CURE method, developed ...