Parameter Estimation Research Articles

Nanoradian-scale precision in light rotation measurement via indefinite quantum dynamics.

The manipulation and metrology of light beams are pivotal for optical science and applications. In particular, achieving ultrahigh precision in the measurement of light beam rotations has been a long-standing challenge. Instead of using quantum probes like entangled photons, we address this challenge by incorporating a quantum strategy called "indefinite time direction" into the parameterizing process of quantum parameter estimation. Leveraging this quantum property of the parameterizing dynamics allows us to maximize the utilization of orbital angular momentum resources for measuring ultrasmall angular rotations of beam profile. Notably, a nanoradian-scale precision of light rotation measurement is lastly achieved in the experiment, which is the highest precision by far to our best knowledge. Furthermore, this scheme holds promise in various optical applications due to the diverse range of manipulable resources offered by photons.

Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

ABSTRACT PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm.

Volume changes and consolidation of expansive subgrades under short- and long-term effects of freeze–thaw cycles

ABSTRACT Pavements and light structures constructed on expansive subgrade layers have experienced severe damage due to volume changes. These layers have been exposed to climatic changes such as freeze–thaw (FT) cycles. Accurate estimation of design parameters regarding heave/settlement is essential for sustainable performance. This work study the instantaneous and long-term effects of successive FT-cycles on the volume stability, swelling, and compressibility characteristics of natural and lime-treated expansive subgrades. Volume changes were traced during successive 15FT cycles. Swelling and consolidation characteristics were studied immediately after FT-cycles. The long-term effect of FT was tested at different recovery periods around year. FT-cycles significantly affects volume changes and compressibility, this effect is proportional to soil type and limited up to a certain number of FT-cycles. During the long-term recovery, a considerable part of underwent deformation is a permanent and not recovered, even after year the soil still memorizes this effect. Due to the propagated cracks, the plastic deformation increased with increase in FT-cycles.

Estimation of Multiple Parameters in Semitransparent Mediums Based on an Improved Grey Wolf Optimization Algorithm

Estimation of Multiple Parameters in Semitransparent Mediums Based on an Improved Grey Wolf Optimization Algorithm

Traction-separation law parameters for the description of age-related changes in the delamination strength of the human descending thoracic aorta.

Aortic dissection is a life-threatening disease that consists in the development of a tear in the wall of the aorta. The initial tear propagates as a discontinuity leading to separation within the aortic wall, which can result in the creation of a so-called false lumen. A fatal threat occurs if the rupture extends through the whole thickness of the aortic wall, as blood may then leak. It is generally accepted that the dissection, which can sometime extend along the entire length of the aorta, propagates via a delamination mechanism. The aim of the present paper is to provide experimentally validated parameters of a mathematical model for the description of the wall's cohesion. A model of the peeling experiment was built in Abaqus. The delamination interface was described by a piecewise linear traction-separation law. The bulk behavior of the aorta was assumed to be nonlinearly elastic, anisotropic, and incompressible. Our simulations resulted in estimates of the material parameters for the traction-separation law of the human descending thoracic aorta, which were obtained by minimizing the differences between the FEM predictions and the delamination force given by the regression of the peeling experiments. The results show that the stress at damage initiation, Tc, should be understood as an age-dependent quantity, and under the assumptions of our model this dependence can be expressed by linear regression as Tc = - 13.03·10-4·Age + 0.2485 if the crack front advances in the axial direction, and Tc = - 7.58·10-4·Age + 0.1897 if the crack front advances in the direction of the aortic circumference (Tc [MPa], Age [years]). Other model parameters were the stiffness K and the separation at failure, δf-δc (K = 0.5MPa/mm, δf-δc = 0.1mm). The material parameters provided by our study can be used in numerical simulations of the biomechanics of dissection propagation through the aorta especially when age-associated phenomena are studied.

Robust point and variance estimation for meta‐analyses with selective reporting and dependent effect sizes

Abstract Meta‐analysis produces a quantitative synthesis of evidence‐based knowledge, shaping not only research trends but also policies and practices in biology. However, two statistical issues, selective reporting and statistical dependence, can severely distort meta‐analytic parameter estimation and inference. Here, we re‐analyse 448 meta‐analyses to demonstrate a new two‐step procedure to deal with two common challenges in biological meta‐analyses that often occur simultaneously: publication bias and non‐independence. First, we employ bias‐robust weighting schemes under the generalized least square estimator to obtain average effect sizes that are more robust to selective reporting. We then use cluster‐robust variance estimation to account for statistical dependence, reducing bias in estimating standard errors and ensuring valid statistical inference. The first step of our approach demonstrates comparable performance in estimating average effect sizes to the existing publication‐bias adjustment methods in the presence of selective reporting. This equivalence holds across two publication bias selection processes. The second step achieves estimates of standard errors consistent with the multilevel meta‐analytic model, a benchmark method with adequate control of Type I error rates for multiple, statistically dependent effect sizes. Re‐analyses of 448 meta‐analyses show that ignoring these two issues tends to overestimate effect sizes by an average of 110% and underestimate standard errors by 120%. To facilitate implementation, we have developed a website including a step‐by‐step tutorial. Complementing current meta‐analytic workflows with the proposed method as a sensitivity analysis can facilitate a transition to a more robust approach in quantitative evidence synthesis.

A Semi-Explicit Algorithm for Parameters Estimation in a Time-Fractional Dual-Phase-Lag Heat Conduction Model

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of a dual-phase-lag heat conduction model with Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag–Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

Radiological characterization of the tailings of an abandoned copper mine using a neural network and geostatistical analysis through the Co-Kriging method

The radiological characterization of soil contaminated with natural radionuclides enables the classification of the area under investigation, the optimization of laboratory measurements, and informed decision-making on potential site remediation. Neural networks (NN) are emerging as a new candidate for performing these tasks as an alternative to conventional geostatistical tools such as Co-Kriging. This study demonstrates the implementation of a NN for estimating radiological values such as ambient dose equivalent (H*(10)), surface activity and activity concentrations of natural radionuclides present in a waste dump of a Cu mine with a high level of natural radionuclides. The results obtained using a NN were compared with those estimated by Co-Kriging. Both models reproduced field measurements equivalently as a function of spatial coordinates. Similarly, the deviations from the reference concentration values obtained in the output layer of the NN were smaller than the deviations obtained from the multiple regression analysis (MRA), as indicated by the results of the root mean square error. Finally, the method validation showed that the estimation of radiological parameters based on their spatial coordinates faithfully reproduced the affected area. The estimation of the activity concentrations was less accurate for both the NN and MRA; however, both methods gave statistically comparable results for activity concentrations obtained by gamma spectrometry (Student's t-test and Fisher's F-test).

The ecological traits of birds in the context of future changes of their ranges under the impact of global climate change

Over the next 70 years, the average annual temperature in Europe is projected to increase by 4.1 °C. In Zhytomyr Oblast, this figure is likely to increase by 4.4 °C. The amount of precipitation in Europe is expected to increase by an average of 60.3 mm per year, with Zhytomyr Oblast experiencing an 87.2 mm per year increase in precipitation compared to the current state. As a consequence of the global climate change, the habitat-preference index will decline for 159 species (60.1%), remain virtually unchanged for 20 species (7.6%), and improve for 83 species (31.7%). The primary factors influencing the differentiation of avian ecological niches by climate regimes are thermal gradient, precipitation gradient, and temporal variability of precipitation throughout the year. With regard to the landscape aspect, bird species can be differentiated according to their campophi­lous/dendrophilous characteristics, their water-intensive/rural or urban/rural tendencies. Soil conditions are a determining factor in the landscape aspect of ecological niches. As a consequence of the global climate change, the habitat preference index will increase for species that are more thermophilic and prefer conditions with higher precipitation. According to predictions, over the coming 70 years, the number of campophilous birds will increase as compared to dendrophilous birds as a result of the global warming. Moisture-loving campophiles will be outcompeted by rural species, and rural species will be outcompeted by urban species. In response to the climate change, populations of bird species are shifting their geographical ranges to regions with suitable climatic conditions. The knowledge of the characteristics of ecological niches is the foundation for anticipating alterations in species distributions in response to global climate change in the future. The estimation of niche parameters is contingent upon the model employed to fit the observed data. The beta model is a flexible and versatile tool that enables comparisons to be made between species complexes. The thermal projection of ecological niche was employed to categorise bird species into ecological groups based on the position of the optimum zone and the width of tolerance amplitude. Ecological groups differ in their prospects in the context of global climate change. It can be predicted that the trends of stenotopic megatherms will improve, whereas those of all other bird species will deteriorate. The most pessimistic scenario is expected for stenotopic microtherms. The analysis of the ecological properties of birds in the region identified 10 functional axes, three of which correlate with the sensitivity of bird species to the global climate change. The prediction of impact of landscape factors on species distribution is challenging. However, the structure of the functional axes allows us to identify which ecological niche parameters, determined by landscape conditions, are more sensitive to birds. Functional axes 1 and 6 indicate a positive trend in urban bird life in the context of global climate change. It is anticipated that positive prospects will emerge in connection with the global climate change for birds belonging to the families Anseriformes, Podicipediformes, Gruiformes, Charadriiformes, Gaviiformes, and Pelecaniformes. Conversely, negative prospects are expected for forest birds (Columbiformes, Strigiformes, Piciformes) and birds of forest and open spaces (Falconiformes). The functional axis 1 correlated with bird weight. A positive correlation between bird weight and the predicted change in the habitat-preference index over the next 70 years was identified exclusively for species belonging to Charadriiformes. The positive effect of weight on the probability of habitat improvement within Charadriiformes can be attributed to the fact that rurally occurring species within this order are usually heavier than those in natural ecosystems, and those in urban habitats are heavier than those in rural habitats. Functional Axis 4 identifies the group of urban birds whose habitat conditions are likely to deteriorate in the near future due to global warming. This group includes woodpeckers, which can be considered an indicator of the biodiversity of bird communities that are sensitive to the global climate change.

Bayesian estimation of covariate assisted principal regression for brain functional connectivity.

This paper presents a Bayesian reformulation of covariate-assisted principal regression for covariance matrix outcomes to identify low-dimensional components in the covariance associated with covariates. By introducing a geometric approach to the covariance matrices and leveraging Euclidean geometry, we estimate dimension reduction parameters and model covariance heterogeneity based on covariates. This method enables joint estimation and uncertainty quantification of relevant model parameters associated with heteroscedasticity. We demonstrate our approach through simulation studies and apply it to analyze associations between covariates and brain functional connectivity using data from the Human Connectome Project.

Modeling of LCF Behaviour on AISI316L Steel Applying the Armstrong–Frederick Kinematic Hardening Model

The combination of kinematic and isotropic hardening models makes it possible to model the behaviour of cyclic elastic-plastic steel material, though the estimation of the hardening parameters and catching the influence of those parameters on the material response is a challenging task. In the current work, an approach for the numerical simulation of the low-cycle fatigue of AISI316L steel is presented using a finite element method to study the fatigue behaviour of the steel at different strain amplitudes and operating temperatures. Fully reversed uniaxial LCF tests are performed at different strain amplitudes and operating temperatures. Based on the LCF test experimental results, the non-linear isotropic and kinematic hardening parameters are estimated for numerical simulation. On comparing, the numerical simulation results were in very good agreement with those of the experimental ones. This presented method for the numerical simulation of the low-cycle fatigue on AISI316 stainless steel can be used for the approximate prediction of the fatigue life of the components under different cyclic loading amplitudes.

L1 Adaptive Fault-Tolerant Control for Nonlinear Systems Subject to Input Constraint and Multiple Faults

This paper investigates an L1 adaptive fault-tolerant control scheme for nonlinear systems with input constraint, external disturbances, and multiple faults, which include actuator faults and sensor faults. Faults and input constraint are important factors that affect the stability and performance of a control system. Actuators and sensors are the most vulnerable components, with the former receiving more attention in comparison. In this paper, sensor faults are first transformed into pseudo-actuator faults through the augmented matrix approach, which facilitates their handling together with actuator faults. Saturation constraints on the control signal are not conducive to the design of the controller. The conversion of an input-saturated function to a time-varying linear system is completed based on function approximation and Lagrange’s mean value theorem. Moreover, a nonlinear system with unknown input gain and uncertainties is constructed using these methods. Next, an L1 adaptive fault-tolerant controller is designed to cope with uncertainties, including system uncertainties, external disturbances, faults, and approximation errors. In the L1 adaptive controller, the online estimation of the time-varying parameters allows for updating of the system state, while the combination of the two is transmitted to the control law such that it can compensate for the effects of the uncertainties. The stability and performance boundaries are further derived using the Lyapunov theory and the L1 reference system. Finally, simulations are carried out to demonstrate the effectiveness of the proposed controller.

Ranging Offset Calibration and Moving Average Filter Enhanced Reliable UWB Positioning in Classic User Environments

With the rapid development of the Internet of Things (IoT), the positioning accuracy requirement of the IoT is increasing, especially for those applications without Global Navigation Satellite System (GNSS) signals. Ultra-Wideband (UWB) is treated as a high-accuracy positioning method that can be utilized in GNSS-blocked environments. However, UWB’s performance is still limited when it is applied in practical applications due to errors such as Non-Line-of-Sight (NLOS) errors, multipath errors, and systematic errors in UWB range values. To constrain the impacts of these mentioned errors on UWB positioning accuracy, this work proposes a novel UWB positioning model by introducing a UWB ranging offset calibration algorithm and a moving average filter into a robust extended Kalman filter. In such a UWB positioning model, the ranging offset calibration algorithm is employed to limit the infuence of UWB systematic errors, and the prior residual-based IGG-III weighting model is used to restrain the impacts of NLOS and multipath errors. The moving average filter is to further decrease the impact of the measuring noise on UWB positioning parameter estimation. To investigate the effectiveness of this proposed method, three sets of UWB experiments are arranged in three classic user environments. The experimental results show that (1) after applying the UWB ranging offset calibration algorithm, UWB positioning accuracies in classic environments, namely indoor condition, outdoor condition, and transition area are increased by 50.3%, 20.2%, and 46.9%, respectively; (2) the moving average filter can effectively improve the smoothness of UWB positioning results in terms of standard deviation; (3) the prior residual-based robust theory brings about 49.4% and 25.2% positioning improvements to horizontal and vertical components under poor measurement quality conditions, but such improvements are rather slight when there are good-quality measurements; and (4) after applying the ranging offset calibration algorithm and moving average filter to the robust EKF together, the elevation accuracy of UWB positioning is increased by 67.1%, 22.2%, and 50.5%, respectively, in the three classic user environments.

DOA Estimation Based on Virtual Array Aperture Expansion Using Covariance Fitting Criterion

Providing higher precision Direction of Arrival (DOA) estimation has become a hot topic in the field of array signal processing for parameter estimation in recent years. However, when the physical aperture of the actual array is small, its aperture limitation means that even with super-resolution estimation algorithms, the achievable estimation precision is limited. This paper takes a novel approach by constructing an optimization algorithm using the covariance fitting criterion based on the array output’s covariance matrix to fit and obtain the covariance matrix of a large aperture virtual array, thereby providing high-precision angular resolution through virtual aperture expansion. The covariance fitting expansion analysis and discussion are unfolded for both uniform linear arrays (ULAs) and sparse linear arrays (SLAs) under four different scenarios. Theoretical analysis and simulation experiments demonstrate that these methods can enhance the effective performance of angle estimation, especially in low signal-to-noise ratios (SNRs) and at small angular intervals by fitting virtual extended aperture data.

Anemia From Inflammation After Intracerebral Hemorrhage and Relationships With Outcome.

Baseline anemia is associated with poor intracerebral hemorrhage (ICH) outcomes. However, underlying drivers for anemia and whether anemia development after ICH impacts clinical outcomes are unknown. We hypothesized that inflammation drives anemia development after ICH and assessed their relationship to outcomes. Patients with serial hemoglobin and iron biomarker concentrations from the HIDEF (High-Dose Deferoxamine in Intracerebral Hemorrhage) trial were analyzed. Adjusted linear mixed models assessed laboratory changes over time. Of 42 patients, significant decrements in hemoglobin occurred with anemia increasing from 19% to 45% by day 5. Anemia of inflammation iron biomarker criteria was met in 88%. A separate cohort of 521 patients with ICH with more granular serial hemoglobin and long-term neurological outcome data was also investigated. Separate regression models assessed whether (1) systemic inflammatory response syndrome (SIRS) scores related to hemoglobin changes over time and (2) hemoglobin changes related to poor 90-day outcome. In this cohort, anemia prevalence increased from 30% to 71% within 2 days of admission yet persisted beyond this time. Elevated systemic inflammatory response syndrome was associated with greater hemoglobin decrements over time (adjusted parameter estimate: -0.27 [95% CI, -0.37 to -0.17]) and greater hemoglobin decrements were associated with poor outcomes (adjusted odds ratio per 1 g/dL increase, 0.76 [95% CI, 0.62-0.93]) independent to inflammation and ICH severity. We identified novel findings that acute anemia development after ICH is common, rapid, and related to inflammation. Because anemia development is associated with poor outcomes, further work is required to clarify if anemia, or its underlying drivers, are modifiable treatment targets that can improve ICH outcomes. https://www.clinicaltrials.gov Unique identifier: NCT01662895.

Ordering the alphabet soup: Strategies to improve consistency and develop a framework of tools for fisheries science vocabulary

Quantitative methods for assessing the status of marine fish populations began in the 19th century with simple catch-curve analysis and have evolved through the decades to use more advanced integrated statistical modeling. The corresponding assessment documents that communicate model estimates to other scientists and fisheries managers, generally, have grown in length and complexity, often describing hundreds of parameter estimates and population dynamics. These documents use a wide range of synonyms or similar terms that can vary either within a single assessment document or among other documents within or across fisheries management regions. The lack of standardization of terms used in assessment documents can lead to challenges in scientific reviews, communicating scientific results to fisheries managers, and for understanding assessments from other management regions. We analyzed the text of 134 assessment documents used to communicate the results of assessment software to managers from the varying management regions in the United States, Australia, and European International Council for the Exploration of the Sea (ICES) to quantify the presence of synonyms or similar terms that are often used interchangeably and highlight ways in which the output language could be harmonized without modifying meaning. Next, we developed an open source web-based dictionary and schema for fisheries science where the community can propose and adopt terms to create an agreed upon terminology. Finally, looking forward to future development, we propose approaches to leverage pre-existing frameworks such as Google coding standards to guide the development of tools to merge long-term ecological data sets, name objects in code bases, and report output. While this guidance is tailored to fisheries practitioners working within the U.S. management system, we aim to align with international standards to increase consistency across the international fisheries science community.

Knowing what to know: Implications of the choice of prior distribution on the behavior of adaptive design optimization.

Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro et al., 2010). ADO dynamically identifies stimuli that, in expectation, yield the most information about a hypothetical construct of interest (e.g., parameters of a cognitive model). To calculate this expectation, ADO leverages the modeler's existing knowledge, specified in the form of a prior distribution. Informative priors align with the distribution of the focal construct in the participant population. This alignment is assumed by ADO's internal assessment of expected information gain. If the prior is instead misinformative, i.e., does not align with the participant population, ADO's estimates of expected information gain could be inaccurate. In many cases, the true distribution that characterizes the participant population is unknown, and experimenters rely on heuristics in their choice of prior and without an understanding of how this choice affects ADO's behavior. Our work introduces a mathematical framework that facilitates investigation of the consequences of the choice of prior distribution on the efficiency of experiments designed using ADO. Through theoretical and empirical results, we show that, in the context of prior misinformation, measures of expected information gain are distinct from the correctness of the corresponding inference. Through a series of simulation experiments, we show that, in the case of parameter estimation, ADO nevertheless outperforms other design methods. Conversely, in the case of model selection, misinformative priors can lead inference to favor the wrong model, and rather than mitigating this pitfall, ADO exacerbates it.

Enhanced Remote Sensing Image Compression Method Using Large Network with Sparse Extracting Strategy

Deep neural networks based on hyper-encoders play a critical role in estimating prior distributions in remote sensing image compression issues. However, most of the existing encoding methods suffer from a problem on the hyper-encoding side, namely the mismatch of extraction ability with the encoder. This ability bias results in likelihood features that fail to extract sufficient information from latent representations. To solve this problem, the feature extraction capabilities of the hyper-encoder are enhanced to better estimate the Gaussian likelihood of the latent representation in end-to-end network optimization. Specifically, residual blocks and a parameter estimation module are incorporated to balance the performance of the encoder and the hyper-encoder. Furthermore, it is observed that the well-trained compression model tends to generate a fixed pattern of latent representations. Therefore, we incorporate a nonlocal cross-channel graph (NCG) on the backside of the encoder. Specifically, it aggregates features between similar latent representations in a graphical manner to further enhance the side information extraction capability of the hyper-encoder. Considering the computational cost, a sparse graph strategy is further developed to dynamically select the most relevant latent representations for aggregation operations, which greatly reduces the computational effort. The proposed algorithm is named nonlocal cross-channel efficient graph (NCEG). A long-dependent residual network is selected as the backbone, and a sparse attention module is inserted into the encoder/decoder side to enhance the perceptual field of the network. The experimental results on two evaluation datasets demonstrate that the proposed method achieves satisfactory results compared to other learning-based methods.

Robust Design Based on Cost-Quality Model in Micro-Manufacturing

This paper proposes a novel total cost model for the micro‐products' entire life cycle that takes into account the uncertainty of the model parameters. The total cost includes pre-sale manufacturing and post-sale warranty costs. Additionally, different marketing strategies are also given based on the weight of internal and external costs. Furthermore, limited data and unknown effects in experiments may cause large errors in parameter estimates. This could prevent the achievement of reliable designs. To address this, robust optimization and interval estimation are used. This approach reduces the impact of uncertainty on parameter estimates. It ensures optimality and robustness in micro-manufacturing parameters. Example analysis and numerical simulation results show that the proposed method assists companies in selecting the optimal manufacturing parameter level that aligns with their marketing strategies. Besides, considering uncertainty factors can ensure that the optimization results remain guaranteed, even under the worst-case scenarios.

Different Statistical Inference Algorithms for the New Pareto Distribution Based on Type-II Progressively Censored Competing Risk Data with Applications

In this research, the statistical inference of unknown lifetime parameters is proposed in the presence of independent competing risks using a progressive Type-II censored dataset. The lifetime distribution associated with a failure mode is assumed to follow the new Pareto distribution, with consideration given to two distinct competing failure reasons. Maximum likelihood estimators (MLEs) for the unknown model parameters, as well as reliability and hazard functions, are derived, noting that they are not expressible in closed form. The Newton–Raphson, expectation maximization (EM), and stochastic expectation maximization (SEM) methods are employed to generate maximum likelihood (ML) estimations. Approximate confidence intervals for the unknown parameters, reliability, and hazard rate functions are constructed using the normal approximation of the MLEs and the normal approximation of the log-transformed MLEs. Additionally, the missing information principle is utilized to derive the closed form of the Fisher information matrix, which, in turn, is used with the delta approach to calculate confidence intervals for reliability and hazards. Bayes estimators are derived under both symmetric and asymmetric loss functions, with informative and non-informative priors considered, including independent gamma distributions for informative priors. The Monte Carlo Markov Chain sampling approach is employed to obtain the highest posterior density credible intervals and Bayesian point estimates for unknown parameters and reliability characteristics. A Monte Carlo simulation is conducted to assess the effectiveness of the proposed techniques, with the performances of the Bayes and maximum likelihood estimations examined using average values and mean squared errors as benchmarks. Interval estimations are compared in terms of average lengths and coverage probabilities. Real datasets are considered and examined for each topic to provide illustrative examples.