Results 41 to 50 of about 2,103,105 (303)
Bayesian Sequential Estimation
The Annals of Statistics, 1977 For fixed $\theta$, let $X_1, X_2, \cdots$ be a sequence of independent identically distributed random variables having density $f_\theta(x)$. Using a sequential Bayes decision theoretic approach we consider the problem of estimating any strictly monotone function $g(\theta)$ when the error incurred by a wrong estimate is measured by squared error loss
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Bayesian autoregressive spectral estimation
2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2021 Autoregressive (AR) time series models are widely used in parametric spectral estimation (SE), where the power spectral density (PSD) of the time series is approximated by that of the \emph{best-fit} AR model, which is available in closed form. Since AR parameters are usually found via maximum-likelihood, least squares or the method of moments, AR ...
Alejandro Cuevas +3 more
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Bayesian estimation of reliability [PDF]
, 2005 Most of statistical theory thought at the undergraduate level is based on classical probability or frequentist probability. Also, the estimation techniques widely used are maximum likelihood estimation (MLE) and methods of moment.
Rahmi, Depriwana
core
Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
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Estimate the parameters of Exponential-Rayleigh distribution, by using Bayesian method
Ibn Al-Haitham Journal for Pure and Applied SciencesIn this paper, point estimation method for parameters α and λ of the parameters of the Exponential-Rayleigh distribution have been estimated by the use of a simulation technique by using two Bayesian estimation methods; the first Bayesian method of ...
Maral Mohammed, Iden Hassan
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Estimation of Reliability in Multi-Component Stress-Strength Model Following Exponentiated Pareto Distribution [PDF]
The Egyptian Statistical Journal, 2012 This article deals with the Bayesian and non-Bayesian estimation of reliability of an s-out-of-k system with identical component strengths which are subjected to a common stress. Assuming that both stress and strength are assumed to have an exponentiated
Heba M.Basheikh, Amal S.Hassan
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Bayesian Integration in Force Estimation [PDF]
Journal of Neurophysiology, 2004 When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and ...
Kording, K., Ku, S., Wolpert, D.
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A Hierarchical Bayesian Model for Frame Representation [PDF]
, 2010 In many signal processing problems, it is fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyperparameters characterizing the probability distribution of the frame
Amel Benazza-Benyahia +9 more
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Molecular Oncology, EarlyView.Tumour heterogeneity and clonal evolution of metastatic salivary gland cancer were evaluated in two patients with adenoid carcinoma and one patient with myoepithelial carcinoma. Radiology‐guided autopsy enabled multi‐region sampling (total samples n = 149), followed by whole‐genome sequencing and phylogenetic reconstruction (17 tumour samples, 4–7 per ...
Gerben Lassche +10 more
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Bayesian Estimation of Graph Signals
IEEE Transactions on Signal Processing, 2022 We consider the problem of recovering random graph signals from nonlinear measurements. For this case, closed-form Bayesian estimators are usually intractable and even numerical evaluation of these estimators may be hard to compute for large networks. In this paper, we propose a graph signal processing (GSP) framework for random graph signal recovery ...
Ariel Kroizer +2 more
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