Min. Mean Std. dev. Median Max. posterior mean of λD 0.0698 0.2016 0.0447 0.2009 0.3468 MLE of λD 0 0.214 0.103 0.1893 0.8542 posterior mean of λP 0.0526 0.1899 0.0638 0.1892 0.4135 MLE of λP 0 0.1876 0.1427 0.1579 0.7812 posterior mean of μD 0.032 0.0968 0.0397 0.0905 0.251 MLE of μD 0 0.1182 0.1469 0.072 0.8741 posterior mean of μP 0.0277 0.1779 0.0936 0.1601 0.5755 MLE of μP 0 0.2108 0.3619 ...

We study two Bayesian (Reference Intrinsic and Jeffreys prior), frequentist (MLE PWM) approaches the nonparametric Hill estimator for Pareto related distributions. Three of these are compared in a simulation all four to investigate how much equity risk capital banks subject Basel II banking regulations must hold. The Reference approach, which is invariant under one-to-one transformations data p...

Abstract Accurate estimation of tuber size distribution (TSD) parameters in discretely categorized potato ( Solanum tuberosum L ) yield samples is desirable for estimating modal sizes, which fundamental to prediction. In the current work, systematic digs were conducted on five commercial fields N = 119) compare Weibull, Gamma and Gaussian functions relative-likelihood-based goodness-of-fit obse...

Abstract In this work, we explore the use of an iterative Bayesian Monte Carlo (iBMC) method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library (TENDL) framework. The goal is to probe model and parameter space code system find optimal sets that reproduces selected experimental data. involves simultaneous variation many reaction models as well their parameters included in ...

ABSTRACT Variable γ-ray emission from blazars, one of the most powerful classes astronomical sources featuring relativistic jets, is a widely discussed topic. In this work, we present results variability study sample 20 blazars using (0.1–300 GeV) observations Fermi/LAT telescope. Using maximum likelihood estimation (MLE) methods, find that probability density functions best describe blazar flu...

Maximum Likelihood Estimation (MLE) suffers from data sparsity problem in sequence prediction tasks where training resource is rare. In order to alleviate this problem, in this paper, we propose a novel generative bridging network (GBN) to train sequence prediction models, which contains a generator and a bridge. Unlike MLE directly maximizing the likelihood of the ground truth, the bridge exte...

Supplementary Methods 6 MAPPING METHOD 6 General theory behind likelihood maximization 7 MLE for expected degrees κ 7 MLE for angular coordinates θ 8 MLE kernels 8 First MLE wrapper 8 Algorithm 1 9 Second MLE wrapper 9 Algorithm 2 10 Parameter estimation and finite size effects 10 Estimating γ 10 Estimating N and k̄ 11 Estimating β 12 DEALING WITH NEW-COMING ASs 12 SENSITIVITY TO MISSING LINKS 1...

Aims. Investigating stochastically driven pulsation puts strong requirements on the quality of (observed) pulsation frequency spectra, such as the accuracy of frequencies, amplitudes, and mode life times and – important when fitting these parameters with models – a realistic error estimate which can be quite different to the formal error. As has been shown by other authors, the method of fittin...

 Minimax estimation problems with restricted parameter space reached increasing interest within the last two decades Some authors derived minimax and admissible estimators of bounded parameters under squared error loss and scale invariant squared error loss In some truncated estimation problems the most natural estimator to be considered is the truncated version of a classic...

This note studies the bias arises from the MLE estimate of the rate parameter and the mean parameter of an exponential distribution. 1 Motivation Although maximum likelihood estimation (MLE) methods provide estimates that are useful, the estimates themselves are not guaranteed to be unbiased. Nevertheless, MLE methods are still highly regarded in practice due to several of their properties, not...