The Maximum a Posterior Estimation of Darts

In this paper, we present a novel approach for joint decorrelation and despeckling of synthetic aperture radar (SAR) imagery. An iterative maximum a posterior estimation is performed to obtain the correlation and speckle-free SAR data, which incorporates a correlation model which realistically explores the physical correlated process of speckle noise on signal in SAR imaging. The correlation model is determined automatically via Bayesian estimation in the log-Fourier domain and patch-wise computation is used to account for spatial nonstationarities existing in SAR data. The proposed approach is compared to a state-of-the-art despeckling technique using both simulated and real SAR data. Experimental results illustrate its improvement in preserving the structural detail, especially the sharpness of the edges, when suppressing speckle noise.

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Fast effect size shrinkage software for beta-binomial models of allelic imbalance

F1000Research ◽

10.12688/f1000research.20916.2 ◽

2020 ◽

Vol 8 ◽

pp. 2024

Author(s):

Joshua P. Zitovsky ◽

Michael I. Love

Keyword(s):

Allelic Imbalance ◽

Real Data ◽

Shrinkage Estimators ◽

Data Set ◽

Bayesian Shrinkage ◽

In Cis ◽

Posterior Estimation ◽

Binomial Models ◽

Better Than ◽

Diploid Organism

Allelic imbalance occurs when the two alleles of a gene are differentially expressed within a diploid organism and can indicate important differences in cis-regulation and epigenetic state across the two chromosomes. Because of this, the ability to accurately quantify the proportion at which each allele of a gene is expressed is of great interest to researchers. This becomes challenging in the presence of small read counts and/or sample sizes, which can cause estimators for allelic expression proportions to have high variance. Investigators have traditionally dealt with this problem by filtering out genes with small counts and samples. However, this may inadvertently remove important genes that have truly large allelic imbalances. Another option is to use pseudocounts or Bayesian estimators to reduce the variance. To this end, we evaluated the accuracy of four different estimators, the latter two of which are Bayesian shrinkage estimators: maximum likelihood, adding a pseudocount to each allele, approximate posterior estimation of GLM coefficients (apeglm) and adaptive shrinkage (ash). We also wrote C++ code to quickly calculate ML and apeglm estimates and integrated it into the apeglm package. The four methods were evaluated on two simulations and one real data set. Apeglm consistently performed better than ML according to a variety of criteria, and generally outperformed use of pseudocounts as well. Ash also performed better than ML in one of the simulations, but in the other performance was more mixed. Finally, when compared to five other packages that also fit beta-binomial models, the apeglm package was substantially faster and more numerically reliable, making our package useful for quick and reliable analyses of allelic imbalance. Apeglm is available as an R/Bioconductor package at http://bioconductor.org/packages/apeglm.

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Alternating imputation posterior estimation of models with crossed random effects

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2010.04.015 ◽

2011 ◽

Vol 55 (1) ◽

pp. 12-25 ◽

Cited By ~ 34

Author(s):

S.-J. Cho ◽

S. Rabe-Hesketh

Keyword(s):

Random Effects ◽

Crossed Random Effects ◽

Posterior Estimation

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Improving power posterior estimation of statistical evidence

Statistics and Computing ◽

10.1007/s11222-013-9397-1 ◽

2013 ◽

Vol 24 (5) ◽

pp. 709-723 ◽

Cited By ~ 25

Author(s):

Nial Friel ◽

Merrilee Hurn ◽

Jason Wyse

Keyword(s):

Statistical Evidence ◽

Posterior Estimation

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An Improved Maximum a Posterior-Based Estimation Method Coping with Capture Effect for RFID Tags Identification

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420500111 ◽

2019 ◽

Vol 34 (05) ◽

pp. 2050011 ◽

Cited By ~ 2

Author(s):

Liqian Zhang ◽

Xueliang Fu ◽

Honghui Li ◽

Juan Li

Keyword(s):

Posterior Probability ◽

Estimation Error ◽

Estimation Method ◽

Wireless Channel ◽

Rfid Tags ◽

Probability Method ◽

Capture Effect ◽

Tag Identification ◽

Lower Estimation ◽

Maximum A Posterior

Capture effect is a very common phenomenon in wireless channel. By using capture effect, the collision tags can be identified in a certain probability, so the identification efficiency and speed of the system can be improved. In this paper, we extend the Maximum a Posterior probability method to capture effect environment and propose a novel tag anti-collision algorithm MAPCE for RFID tags identification. The proposed algorithm can not only estimate the number of tags, but also estimate the capture effect probability. Based on the estimation, we can derive the optimal frame length in the tag identification systems. The experimental results show that when the tags’ number is larger than 600 and less than 1000, the proposed algorithm has lower computational complexity and lower estimation error rate.

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A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

Applied Sciences ◽

10.3390/app9030613 ◽

2019 ◽

Vol 9 (3) ◽

pp. 613

Author(s):

Bangcheng Zhang ◽

Yubo Shao ◽

Zhenchen Chang ◽

Zhongbo Sun ◽

Yuankun Sui

Keyword(s):

Kalman Filter ◽

Health Management ◽

Remaining Useful Life ◽

Degradation Model ◽

Time Prediction ◽

Model Based ◽

Stochastic Parameters ◽

Deterioration Process ◽

Useful Life ◽

Posterior Estimation

Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method

Forests ◽

10.3390/f11121369 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1369

Author(s):

Chenjian Liu ◽

Xiaoman Zheng ◽

Yin Ren

Keyword(s):

Sensitivity Analysis ◽

Parameter Optimization ◽

Bayesian Method ◽

Growth Models ◽

Probability Distributions ◽

Model Parameters ◽

Simulation Accuracy ◽

Sensitivity Analysis Method ◽

Improved Model ◽

Posterior Estimation

Sensitivity analysis and parameter optimization of stand models can improve their efficiency and accuracy, and increase their applicability. In this study, the sensitivity analysis, screening, and optimization of 63 model parameters of the Physiological Principles in Predicting Growth (3PG) model were performed by combining a sensitivity analysis method and the Markov chain Monte Carlo (MCMC) method of Bayesian posterior estimation theory. Additionally, a nine-year observational dataset of Chinese fir trees felled in the Shunchang Forest Farm, Nanping, was used to analyze, screen, and optimize the 63 model parameters of the 3PG model. The results showed the following: (1) The parameters that are most sensitive to stand stocking and diameter at breast height (DBH) are nWs(power in stem mass vs. diameter relationship), aWs(constant in stem mass vs. diameter relationship), alphaCx(maximum canopy quantum efficiency), k(extinction coefficient for PAR absorption by canopy), pRx(maximum fraction of NPP to roots), pRn(minimum fraction of NPP to roots), and CoeffCond(defines stomatal response to VPD); (2) MCMC can be used to optimize the parameters of the 3PG model, in which the posterior probability distributions of nWs, aWs, alphaCx, pRx, pRn, and CoeffCond conform to approximately normal or skewed distributions, and the peak value is prominent; and (3) compared with the accuracy before sensitivity analysis and a Bayesian method, the biomass simulation accuracy of the stand model was increased by 13.92%, and all indicators show that the accuracy of the improved model is superior. This method can be used to calibrate the parameters and analyze the uncertainty of multi-parameter complex stand growth models, which are important for the improvement of parameter estimation and simulation accuracy.

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