Results 11 to 20 of about 2,020,572 (302)

Bayesian estimation of a subspace [PDF]

open access: yes2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011
We consider the problem of subspace estimation in a Bayesian setting. First, we revisit the conventional minimum mean square error (MSE) estimator and explain why the MSE criterion may not be fully suitable when operating in the Grassmann manifold. As an alternative, we propose to carry out subspace estimation by minimizing the mean square distance ...
Olivier Besson   +2 more
openaire   +1 more source

Bayesian Estimation of Turbulent Motion [PDF]

open access: yesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
Based on physical laws describing the multiscale structure of turbulent flows, this paper proposes a regularizer for fluid motion estimation from an image sequence. Regularization is achieved by imposing some scale invariance property between histograms of motion increments computed at different scales.
Patrick Héas   +4 more
openaire   +5 more sources

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

open access: yesComputational Journal of Mathematical and Statistical Sciences, 2023
Generalized power Akshaya distribution is a brand-new two-parameter distribution that builds on the Akshaya distribution first introduced by \cite{Ramadan}. The lifetime data is intended to be modelled by this distribution.
Ahlam Hamdy, Ehab M. Almetwally
doaj   +1 more source

Applying Monte Carlo Simulations to a Small Data Analysis of a Case of Economic Growth in COVID-19 Times

open access: yesSAGE Open, 2023
Studies on the going-on COVID-19 pandemic face small sample issues. In this context, Bayesian estimation is considered a viable alternative to frequentist estimation.
Nguyen Ngoc Thach
doaj   +1 more source

Bayesian Estimation With Distance Bounds [PDF]

open access: yesIEEE Signal Processing Letters, 2012
We consider the problem of estimating a random state vector when there is information about the maximum distances between its subvectors. The estimation problem is posed in a Bayesian framework in which the minimum mean square error (MMSE) estimate of the state is given by the conditional mean.
Dave Zachariah   +3 more
openaire   +3 more sources

Accelerated Life Test Method for the Doubly Truncated Burr Type XII Distribution

open access: yesMathematics, 2020
The Burr type XII (BurrXII) distribution is very flexible for modeling and has earned much attention in the past few decades. In this study, the maximum likelihood estimation method and two Bayesian estimation procedures are investigated based on ...
Hua Xin   +3 more
doaj   +1 more source

Bayesian method application: Integrating mathematical modeling into clinical pharmacy through vancomycin therapeutic monitoring

open access: yesPharmacology Research & Perspectives, 2022
The most recent consensus guidelines for dosing and monitoring vancomycin recommended the use of area‐under‐the‐curve with Bayesian estimation for therapeutic monitoring.
Ashley Chen   +3 more
doaj   +1 more source

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data. [PDF]

open access: yesPLoS ONE, 2016
On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain ...
Kai Liu   +3 more
doaj   +1 more source

Bayesian nonparametric subspace estimation [PDF]

open access: yes2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
Principal component analysis is a widely used technique to perform dimension reduction. However, selecting a finite number of significant components is essential and remains a crucial issue. Only few attempts have proposed a probabilistic approach to adaptively select this number. This paper introduces a Bayesian nonparametric model to jointly estimate
Elvira, Clément   +2 more
openaire   +2 more sources

Approximate Bayesian inference for doubly robust estimation [PDF]

open access: yes, 2015
Doubly robust estimators are typically constructed by combining outcome regression and propensity score models to satisfy moment restrictions that ensure consistent estimation of causal quantities provided at least one of the component models is ...
McCoy, EJ, Graham, DJ, Stephens, DA
core   +1 more source

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