We propose a new model selection method, named the posterior averaging information criterion, for Bayesian model assessment to minimize the risk of predicting independent future observations. The theoretical foundation is built on the Kullback–...
We study inference and prediction for two populations whose lifetimes follow two-parameter Birnbaum–Saunders distributions under a joint progressive Type-II censoring scheme. We derive the observed-data likelihood and obtain maximum likelihood...
Quantifying geographic variation is crucial for policy evaluation, yet researchers often rely on complex national surveys not designed for sub-national inference. This design-analysis mismatch creates two challenges when decomposing variance across d...
We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise are carried out using distinct (but interacting) methods. More specifically, we co...
The progressive-stress accelerated life test is discussed under the ordered ranked set sampling procedure. It is assumed that the lifetime of an item under use stress is exponentially distributed and the law of inverse power is considered as the rela...
This paper explores statistical inferences for the maximum ranked set sampling with unequal samples (MaxRSSU) from the Burr Type-III distribution. Under the assumption that the differences between different multiple MaxRSSU cycles are non-ignorable,...
Exponentially weighted moving average (EWMA) and Shewhart control charts are commonly utilized to detect the small to moderate and large shifts in the process mean, respectively. This article introduces a novel Bayesian AEWMA control chart that emplo...
Sampling from high-dimensional posterior distributions is a central challenge in Bayesian inference and noisy inverse problems. Standard first-order Langevin-based methods often suffer from slow convergence and sensitivity to step-size hyperparameter...
In the 1920s, the English philosopher W.E. Johnson introduced a characterization of the symmetric Dirichlet prior distribution in terms of its predictive distribution. This is typically referred to as Johnson’s “sufficientness” postulate, and it has...
Abstract: This paper is concerned with applying the Bayesian and E-Bayesian approaches to estimating the unknown parameters of the modified Topp–Leone–Chen distribution under a progressive Type-II censored sample plan. The paper explores...
Nested sampling is an efficient algorithm for the calculation of the Bayesian evidence and posterior parameter probability distributions. It is based on the step-by-step exploration of the parameter space by Monte Carlo sampling with a series of valu...
Bayesian estimation of nonlinear mixed-effects models typically relies on Markov-Chain Monte Carlo (MCMC) methods due to the intractability of the posterior distribution. While widely used for longitudinal data with missing observations, the performa...
This research tackles the challenge of forecasting nonlinear time series data with stochastic structural variations by proposing the Markov switching autoregressive model with time-varying parameters (MSAR-TVP). Although effective in modeling dynamic...
Dependence in meta-analytic models can happen due to the same collected data or from the same researchers. The hierarchical Bayesian linear model in a meta-analysis that allows dependence in effect sizes is investigated in this paper. The interested...
This article discusses different methods for estimating the shape and scale parameters of the generalized inverted exponential distribution (GIED) and the acceleration factor in constant stress partially accelerated life test (CSPALT) with general pr...
In this paper, we study the statistical inference of the generalized inverted exponential distribution with the same scale parameter and various shape parameters based on joint progressively type-II censored data. The expectation maximization (EM) al...
Different non-Bayesian and Bayesian techniques were used to estimate the pseudo-Lindley (PsL) distribution’s parameters in this study. To derive Bayesian estimators, one must assume appropriate priors on the parameters and use loss functions su...
This paper investigates the statistical inference of inverse power Lomax distribution parameters under progressive first-failure censored samples. The maximum likelihood estimates (MLEs) and the asymptotic confidence intervals are derived based on th...
This paper proposes a Bayesian estimation method for spatial lagged panel quantile models. The proposed model simultaneously considers spatial lag effects of the dependent variable and the quantile regression framework, enabling effective capture of...
Utility functions in Bayesian experimental design are usually based on the posterior distribution. When the posterior is found by simulation, it must be sampled from for each future dataset drawn from the prior predictive distribution. Many thousands...
Bayesian structural model updating (SMU) is among the most powerful methods for estimating the bending stiffness and modal damping of high-speed railway (HSR) bridges and predicting their bridge-response-based resonance responses. Although studies in...
In medical research, random censoring often occurs due to unforeseen subject withdrawals, whereas progressive censoring is intentionally applied to minimize time and resource requirements during experimentation. This work focuses on estimating the pa...
Determining the strength of nonlinear, statistical dependencies between two variables is a crucial matter in many research fields. The established measure for quantifying such relations is the mutual information. However, estimating mutual informatio...
This paper explores the likelihood and Bayesian estimation of the stress–strength reliability parameter (
In this paper, the parameter estimation problem of a truncated normal distribution is discussed based on the generalized progressive hybrid censored data. The desired maximum likelihood estimates of unknown quantities are firstly derived through the...
The point and interval estimations for the unknown parameters of an exponentiated half-logistic distribution based on adaptive type II progressive censoring are obtained in this article. At the beginning, the maximum likelihood estimators are derived...
We present here Nested_fit, a Bayesian data analysis code developed for investigations of atomic spectra and other physical data. It is based on the nested sampling algorithm with the implementation of an upgraded lawn mower robot method for finding...
Both frequentist and Bayesian approaches are presented in this paper for a multiple step-stress accelerated life test. It is assumed that the lifetime distributions of experimental units under each stress level conform to a two-parameter exponentiate...
The Bayesian approach offers a flexible, interpretable and powerful framework for statistical analysis, making it a valuable tool to help in making optimal decisions under uncertainty. It incorporates prior knowledge or beliefs about the parameters,...
Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aim...
24 January 2021
The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients i...
This study investigates statistical inference for upper record ranked set sampling (URRSS) data from the Kies distribution. In multiple-cycle URRSS settings where the heterogeneity across cycles is non-ignorable, both classical and Bayesian approache...
Big data applications have tremendously increased due to technological developments. However, processing such a large amount of data is challenging for machine learning algorithms and computing resources. This study aims to analyze a large amount of...
The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT ap...
An optimized evaluation method of the bearing capacity of reinforced concrete beam based on the Bayesian theory was proposed in this paper. This evaluation method optimized the traditional Markov Chain-Monte Carlo (MCMC) sampling method, and proposed...
In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt) process regression model that assumes a MaxEnt prior distri...
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging s...
As a result of the two-parameter Birnbaum–Saunders (BS) distribution being successful in modelling fatigue failure times, several extensions of this model have been explored from different aspects. In this article, we consider a progressive str...
In reliability engineering and life-testing experiments, exact measurements can be costly or destructive, whereas ranking information and upper record observations are often easier to obtain. Upper record ranked set sampling (RRSS) provides an effici...
The need for accurate physical measurements is omnipresent in both scientific and engineering applications. Such measurements can be used to explore and characterize the behavior of a system over the parameters of interest. These procedures are often...
Particle-based Variational Inference (ParVI) methods have been widely adopted in deep Bayesian inference tasks such as Bayesian neural networks or Gaussian Processes, owing to their efficiency in generating high-quality samples given the score of the...
Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of p...
Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important informa...
This paper compares two combined methods for modeling correlations among multiple random variables in the state-space sampling approach that is adopted in non-sequential Monte Carlo Simulation. The first method combines the Rank Score (Iman and Conov...
Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation requires researchers to decide not only whether estimation has converged but also whether the Bayesian estimates are well-approximated by summary statistics from the chain. On the con...
Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probi...
There are two challenges in the comprehensive marine hazard assessment. The influencing mechanism of marine disaster is uncertain and disaster data are sparse. Aiming at the uncertain knowledge and small sample in assessment modeling, we combine the...
The Markov chain Monte Carlo (MCMC) method based on Metropolis–Hastings (MH) sampling is a popular approach in solving seismic acoustic impedance (AI) inversion problem, as it can improve the inversion resolution by statistical prior informatio...
Clustered data are a complex and frequently used type of data. Traditional factor analysis methods are effective for non-clustered data, but they do not adequately capture correlations between multiple observed individuals or variables in clustered d...
We present a comprehensive comparison of different Markov chain Monte Carlo (MCMC) sampling methods, evaluating their performance on both standard test problems and cosmological parameter estimation. Our analysis includes traditional Metropolis&ndash...
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