Results 21 to 30 of about 165,261 (262)
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 Differential Privacy
CoRR, 2022 17 pages, 8 figures.
Santiago Zanella-Béguelin +8 more
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Nonparametric Bayesian hazard rate models based on penalized splines [PDF]
, 2003 Extensions of the traditional Cox proportional hazard model, concerning the following features are often desirable in applications: Simultaneous nonparametric estimation of baseline hazard and usual fixed covariate effects, modelling and detection of ...
Fahrmeir, Ludwig, Hennerfeind, Andrea
core +1 more source
FEBS Open Bio, EarlyView.This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
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
Using stacking to average bayesian predictive distributions (with discussion) [PDF]
, 2018 Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of ...
Grunwald, Peter +43 more
core +1 more source
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 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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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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FEBS Open Bio, EarlyView.Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young +7 more
wiley +1 more source