162,996 research outputs found
Estimating the polarization degree of polarimetric images in coherent illumination using maximum likelihood methods
This paper addresses the problem of estimating the polarization degree of polarimetric images in coherent illumination. It has been recently shown that the degree of polarization associated to polarimetric images can be estimated by the method of moments applied to two or four images assuming fully developed speckle. This paper shows that the estimation can also be conducted by using maximum likelihood methods. The maximum likelihood estimators of the
polarization degree are derived from the joint distribution of the image intensities. We show that the joint distribution of polarimetric images is a multivariate gamma distribution whose marginals are univariate, bivariate or trivariate gamma distributions. This property is used to derive maximum likelihood estimators of the polarization degree using two, three or four images. The proposed
estimators provide better performance that the estimators of
moments. These results are illustrated by estimations conducted on synthetic and real images
Asymptotic Accuracy of Bayesian Estimation for a Single Latent Variable
In data science and machine learning, hierarchical parametric models, such as
mixture models, are often used. They contain two kinds of variables: observable
variables, which represent the parts of the data that can be directly measured,
and latent variables, which represent the underlying processes that generate
the data. Although there has been an increase in research on the estimation
accuracy for observable variables, the theoretical analysis of estimating
latent variables has not been thoroughly investigated. In a previous study, we
determined the accuracy of a Bayes estimation for the joint probability of the
latent variables in a dataset, and we proved that the Bayes method is
asymptotically more accurate than the maximum-likelihood method. However, the
accuracy of the Bayes estimation for a single latent variable remains unknown.
In the present paper, we derive the asymptotic expansions of the error
functions, which are defined by the Kullback-Leibler divergence, for two types
of single-variable estimations when the statistical regularity is satisfied.
Our results indicate that the accuracies of the Bayes and maximum-likelihood
methods are asymptotically equivalent and clarify that the Bayes method is only
advantageous for multivariable estimations.Comment: 28 pages, 3 figure
Maximum Likelihood Estimation for Single Particle, Passive Microrheology Data with Drift
Volume limitations and low yield thresholds of biological fluids have led to
widespread use of passive microparticle rheology. The mean-squared-displacement
(MSD) statistics of bead position time series (bead paths) are either applied
directly to determine the creep compliance [Xu et al (1998)] or transformed to
determine dynamic storage and loss moduli [Mason & Weitz (1995)]. A prevalent
hurdle arises when there is a non-diffusive experimental drift in the data.
Commensurate with the magnitude of drift relative to diffusive mobility,
quantified by a P\'eclet number, the MSD statistics are distorted, and thus the
path data must be "corrected" for drift. The standard approach is to estimate
and subtract the drift from particle paths, and then calculate MSD statistics.
We present an alternative, parametric approach using maximum likelihood
estimation that simultaneously fits drift and diffusive model parameters from
the path data; the MSD statistics (and consequently the compliance and dynamic
moduli) then follow directly from the best-fit model. We illustrate and compare
both methods on simulated path data over a range of P\'eclet numbers, where
exact answers are known. We choose fractional Brownian motion as the numerical
model because it affords tunable, sub-diffusive MSD statistics consistent with
typical 30 second long, experimental observations of microbeads in several
biological fluids. Finally, we apply and compare both methods on data from
human bronchial epithelial cell culture mucus.Comment: 29 pages, 12 figure
A statistical physics perspective on criticality in financial markets
Stock markets are complex systems exhibiting collective phenomena and
particular features such as synchronization, fluctuations distributed as
power-laws, non-random structures and similarity to neural networks. Such
specific properties suggest that markets operate at a very special point.
Financial markets are believed to be critical by analogy to physical systems
but few statistically founded evidence have been given. Through a data-based
methodology and comparison to simulations inspired by statistical physics of
complex systems, we show that the Dow Jones and indices sets are not rigorously
critical. However, financial systems are closer to the criticality in the crash
neighborhood.Comment: 23 pages, 19 figure
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