Bayesian Analysis Method Research Articles

Semi-parametric analysis of efficiency and productivity using Gaussian processes

SummaryThis paper proposes a fully Bayesian semi-parametric method for efficiency and productivity analysis based on Gaussian processes. The proposed technique frees the researcher from having to specify a functional form for the production frontier, and it is shown in simulated data to perform as well as flexible parametric models when correct distributional assumptions are imposed on the inefficiency component of the error term, and slightly better when incorrect assumptions are made. The technique is applied to a panel dataset of US electric utilities, where total-factor productivity growth is estimated and decomposed with both parametric and semi-parametric techniques.

Bayesian dynamic modeling and monitoring of network flows

AbstractIn the context of a motivating study of dynamic network flow data on a large-scale e-commerce website, we develop Bayesian models for online/sequential analysis for monitoring and adapting to changes reflected in node–node traffic. For large-scale networks, we customize core Bayesian time series analysis methods using dynamic generalized linear models (DGLMs). These are integrated into the context of multivariate networks using the concept of decouple/recouple that was recently introduced in multivariate time series. This method enables flexible dynamic modeling of flows on large-scale networks and exploitation of partial parallelization of analysis while maintaining coherence with an over-arching multivariate dynamic flow model. This approach is anchored in a case study on Internet data, with flows of visitors to a commercial news website defining a long time series of node–node counts on over 56,000 node pairs. Central questions include characterizing inherent stochasticity in traffic patterns, understanding node–node interactions, adapting to dynamic changes in flows and allowing for sensitive monitoring to flag anomalies. The methodology of dynamic network DGLMs applies to many dynamic network flow studies.

Bayesian clinical trials at The University of Texas MD Anderson Cancer Center: An update

In our 2009 article, we showed that Bayesian methods had established a foothold in developing therapies in our institutional oncology trials. In this article, we will document what has happened since that time. In addition, we will describe barriers to implementing Bayesian clinical trials, as well as our experience overcoming them. We reviewed MD Anderson Cancer Center clinical trials submitted to the institutional protocol office for scientific and ethical review between January 2009 and December 2013, the same length time period as the previous article. We tabulated Bayesian methods implemented for design or analyses for each trial and then compared these to our previous findings. Overall, we identified 1020 trials and found that 283 (28%) had Bayesian components so we designated them as Bayesian trials. Among MD Anderson-only and multicenter trials, 56% and 14%, respectively, were Bayesian, higher rates than our previous study. Bayesian trials were more common in phase I/II trials (34%) than in phase III/IV (6%) trials. Among Bayesian trials, the most commonly used features were for toxicity monitoring (65%), efficacy monitoring (36%), and dose finding (22%). The majority (86%) of Bayesian trials used non-informative priors. A total of 75 (27%) trials applied Bayesian methods for trial design and primary endpoint analysis. Among this latter group, the most commonly used methods were the Bayesian logistic regression model (N = 22), the continual reassessment method (N = 20), and adaptive randomization (N = 16). Median institutional review board approval time from protocol submission was the same 1.4 months for Bayesian and non-Bayesian trials. Since the previous publication, the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial was the first large-scale decision trial combining multiple treatments in a single trial. Since then, two regimens in breast cancer therapy have been identified and published from the cooperative Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis (I-SPY 2), enhancing cooperation among investigators and drug developers across the nation, as well as advancing information needed for personalized medicine. Many software programs and Shiny applications for Bayesian trial design and calculations are available from our website which has had more than 21,000 downloads worldwide since 2004. Bayesian trials have the increased flexibility in trial design needed for personalized medicine, resulting in more cooperation among researchers working to fight against cancer. Some disadvantages of Bayesian trials remain, but new methods and software are available to improve their function and incorporation into cancer clinical research.

A spatially explicit empirical model of structural development processes in natural forests based on climate and topography.

Stand structure develops with stand age. Old‐growth forests with well‐developed stand structure support many species. However, development rates of stand structure likely vary with climate and topography. We modeled structural development of 4 key stand variables and a composite old‐growth index as functions of climatic and topographic covariates. We used a hierarchical Bayesian method for analysis of extensive snap‐shot National Forest Inventory (NFI) data in Japan (n = 9244) to account for differences in stand age. Development rates of structural variables and the old‐growth index exhibited curvilinear responses to environmental covariates. Flat sites were characterized by high rates of structural development. Approximately 150 years were generally required to attain high values (approximately 0.8) of the old‐growth index. However, the predicted age to achieve specific values varied depending on environmental conditions. Spatial predictions highlighted regional variation in potential structural development rates. For example, sometimes there were differences of >100 years among sites, even in the same catchment, in attainment of a medium index value (0.5) after timber harvesting. The NFI data suggested that natural forests, especially old natural forests (>150 years), remain generally on unproductive ridges, steep slopes, or areas with low temperature and deep snow, where many structural variables show slow development rates. We suggest that maintenance and restoration of old natural forests on flat sites should be prioritized for conservation due to the likely rapid development of stand structure, although remaining natural forests on low‐productivity sites are still important and should be protected.

The complete mitochondrial genome of Eastern Yellow Wagtail (Motacilla tschutschensis)

Motacilla tschutschensis is a species of small passerine bird belonging to the family Motacillidae. Complete mitochondrial genome of Eastern Yellow Wagtail (M. tschutschensis) has been sequenced in this study. The genome is 16,829 bp in length and consists of 13 protein-coding genes, 2 rRNA genes, 1 control region (D-loop), and 22 tRNA genes. The phylogenetic tree was reconstructed using the Bayesian analysis method, indicating that M. tschutschensis was closely related to Motacillalugens and Motacillaalba.

A spectrometer for high-precision ion temperature and velocity measurements in low-temperature plasmas.

We have developed a low-cost spectrometer with simple optical design that achieves unprecedented precision measurements of ion temperature (±0.01 eV) and velocity (±20 m/s). A Fabry-Pérot étalon provides the simultaneous high resolving power and high throughput needed for the light levels available in singly ionized helium and argon plasmas. Reducing the systematic uncertainty in the absolute wavelength calibration needed for the specified velocity precision motivates a Bayesian analysis method called Nested Sampling to address the nontrivial uncertainty in the diffraction order. An initial emission measurement of a singly charged stationary argon plasma yields a temperature of 0.339 ± 0.007 eV and a velocity of -3 ± 4 m/s with a systematic uncertainty of 20 m/s.

Оцінювання ризику в системі забезпечення безпеки польотів державної авіації України

The commander of the aviation unit makes a decision to conduct flights in conditions of uncertainty. Lack of full input information makes him rely on his own experience and intuition. In this case, the existing regulatory framework does not require the decision maker to carry out risk assessment in relation to the implementation of the decision on the safety of flights. And as a result, quality of decision fully depends on a competence and experience of decision maker. The article considers the possibility of implementation of the theory of risks approaches in the system of ensuring safety of flights of state aviation. A decision-making algorithm based on risk assessment is proposed. The analysis of methods used in civil aviation for risk assessment is carried out. The expediency of their application for conditions of state aviation activity is considered. In order to solve the tasks of assessing the safety risks of state aviation of Ukraine it is proposed to use a combination of Bayesian analysis method, the method of Risk Indicators and the 5x5 hazard/frequency matrix method.

ЭМПИРИЧЕСКОЕ ИССЛЕДОВАНИЕ КРИТИЧЕСКОГО ПЕРЕХОДА В КЛИМАТЕ ПЛЕЙСТОЦЕНА НА ОСНОВЕ НЕЛИНЕЙНОДИНАМИЧЕСКОЙ РЕКОНСТРУКЦИИ

Until now, cause of the Mid-Pleistocene Transition (MPT), when the dominant periodicity of climate cycles changed from 41,000 to 100,000 years in the absence of significant change in orbital forcing, are still an open question in Paleoclimatology. Here we show how a Bayesian data analysis and nonlinear dynamical reconstruction methods can help to reveal the main mechanisms underlying the Pleistocene variability. Our Bayesian data-driven model from benthic d18O records (LR04 stack) accounts the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation. The research was supported by the RAS Presidium Program «Nonlinear dynamics: fundamental problems and applications».

Reliability assessment of a continuous-state fuel cell stack system with multiple degrading components

A polymer electrolyte membrane fuel cell (PEMFC) stack is a multi-component system composed of continuously degrading fuel cells. The voltage degradation of the fuel cells causes the degradation of the stack system, which has two system-level degradation measures; the overall stack output voltage and the minimum voltage of individual cells. This paper develops a hierarchical Bayesian modeling and data analysis method to predict the reliability of a PEMFC stack system using the voltage degradation data collected from its fuel cell components. We introduce a two-term exponential model to describe the nonlinear voltage degradation paths of the fuel cell components, then builds a hierarchical Bayesian degradation model to predict the stack system reliability by taking a k-out-of-m:F system into account. Possible alternative modeling approaches are discussed with an in-depth comparison. This paper will contribute to the modeling and data analysis methods for continuous-state systems composed of continuous-state components.

Accurate Bayesian Data Classification Without Hyperparameter Cross-Validation

We extend the standard Bayesian multivariate Gaussian generative data classifier by considering a generalization of the conjugate, normal-Wishart prior distribution and by deriving the hyperparameters analytically via evidence maximization. The behaviour of the optimal hyperparameters is explored in the high-dimensional data regime. The classification accuracy of the resulting generalized model is competitive with state-of-the art Bayesian discriminant analysis methods, but without the usual computational burden of cross-validation.

Bayesian Survival Analysis Model for Girth Weld Failure Prediction

The formation and development of a dataset for pipeline systems have affected the management and decision-making of pipeline operators. The dataset, combined with a proposed theoretical analysis method, can provide significant improvement for the safe and economic operation of pipelines. On the basis of the pipeline data and its essential impact on pipeline risk assessment, the authors propose for the first time the Staged Bayesian failure model for girth welds of a pipeline, using the “tree-type” accident theory and Bayesian survival analysis method. This model of girth welds is consistent with the distribution of Kaplan–Meier functions and can predict the influence of different factors on the survival probability of girth welds. These new research results can lay the technical foundation for the failure analysis of pipeline girth welds.

Complete Chloroplast Genomes and Comparative Analysis of Sequences Evolution among Seven Aristolochia (Aristolochiaceae) Medicinal Species.

Aristolochiaceae, comprising about 600 species, is a unique plant family containing aristolochic acids (AAs). In this study, we sequenced seven species of Aristolochia, and retrieved eleven chloroplast (cp) genomes published for comparative genomics analysis and phylogenetic constructions. The results show that the cp genomes had a typical quadripartite structure with conserved genome arrangement and moderate divergence. The cp genomes range from 159,308 bp to 160,520 bp in length and have a similar GC content of 38.5%–38.9%. A total number of 113 genes were identified, including 79 protein-coding genes, 30 tRNAs and four rRNAs. Although genomic structure and size were highly conserved, the IR-SC boundary regions were variable between these seven cp genomes. The trnH-GUG genes, are one of major differences between the plastomes of the two subgenera Siphisia and Aristolochia. We analyzed the features of nucleotide substitutions, distribution of repeat sequences and simple sequences repeats (SSRs), positive selections in the cp genomes, and identified 16 hotspot regions for genomes divergence that could be utilized as potential markers for phylogeny reconstruction. Phylogenetic relationships of the family Aristolochiaceae inferred from the 18 cp genome sequences were consistent and robust, using maximum parsimony (MP), maximum likelihood (ML), and Bayesian analysis (BI) methods.

Bayesian Analysis for Extracting Properties of the Nuclear Equation of State from Observational Data Including Tidal Deformability from GW170817

We develop a Bayesian analysis method for selecting the most probable equation of state under a set of constraints from compact star physics, which now include the tidal deformability from GW170817. We apply this method for the first time to a two-parameter family of hybrid equations of state that is based on realistic models for the hadronic phase (KVORcut02) and the quark matter phase (SFM α ) which produce a third family of hybrid stars in the mass–radius diagram. One parameter ( α ) characterizes the screening of the string tension in the string-flip model of quark matter while the other ( Δ P ) belongs to the mixed phase construction that mimics the thermodynamics of pasta phases and includes the Maxwell construction as a limiting case for Δ P = 0 . We present the corresponding results for compact star properties like mass, radius and tidal deformabilities and use empirical data for them in the newly developed Bayesian analysis method to obtain the probabilities for the model parameters within their considered range.

Slope reliability and back analysis of failure with geotechnical parameters estimated using Bayesian inference

A Bayesian approach is proposed for the inference of the geotechnical parameters used in slope design. The methodology involves the construction of posterior probability distributions that combine prior information on the parameter values with typical data from laboratory tests and site investigations used in design. The posterior distributions are often complex, multidimensional functions whose analysis requires the use of Markov chain Monte Carlo (MCMC) methods. These procedures are used to draw representative samples of the parameters investigated, providing information on their best estimate values, variability and correlations. The paper describes the methodology to define the posterior distributions of the input parameters for slope design and the use of these results for evaluation of the reliability of a slope with the first order reliability method (FORM). The reliability analysis corresponds to a forward stability analysis of the slope where the factor of safety (FS) is calculated with a surrogate model from the more likely values of the input parameters. The Bayesian model is also used to update the estimation of the input parameters based on the back analysis of slope failure. In this case, the condition FS = 1 is treated as a data point that is compared with the model prediction of FS. The analysis requires a sufficient number of observations of failure to outbalance the effect of the initial input parameters. The parameters are updated according to their uncertainty, which is determined by the amount of data supporting them. The methodology is illustrated with an example of a rock slope characterised with a Hoek-Brown rock mass strength. The example is used to highlight the advantages of using Bayesian methods for the slope reliability analysis and to show the effects of data support on the results of the updating process from back analysis of failure.

Bayesian integrative analysis for multi-fidelity computer experiments

ABSTRACTThis paper proposes a Bayesian integrative analysis method for linking multi-fidelity computer experiments. Instead of assuming covariance structures of multivariate Gaussian process models, we handle the outputs from different levels of accuracy as independent processes and link them via a penalization method that controls the distance between their overall trends. Based on the priors induced by the penalty, we build Bayesian prediction models for the output at the highest accuracy. Simulated and real examples show that the proposed method is better than existing methods in terms of prediction accuracy for many cases.

Species identification of processed sea cucumbers from Malaysian market based on concatenated gene sequences of mitochondrial rRNA genes

Species identification of sea cucumbers that have undergone body deformation due to extensive food processing, e.g., beche-de-mer, is difficult, especially with the copresence of cases of unlabelled or mislabelled sea cucumber-based products on the market. Therefore, a study was done to determine the species identities of processed sea cucumbers from selected Malaysian markets using concatenated gene sequences of non-protein-coding 12S and 16S mitochondrial rRNA genes. Phylogenetic analyses, based on the distance-based Neighbor Joining method and character-based methods, i.e., the Maximum Parsimony method, Maximum Likelihood method, and the Bayesian Analysis method, of 47 ingroup sequences, representing 37 processed sea cucumber specimens, 6 reference samples, and 4 additional specimens, suggested the presence of 3 main clusters, i.e., gamat family, consisting of genus Stichopus and genus Thelenota; and timun laut family, comprising family Holothuriidae. A number of 3 gamat species, i.e., Stichopus horrens, Stichopus vastus, and Thelenota anax, were recorded. Meanwhile, the specimens of Holothuria (Halodeima) atra, Holo-thuria (Halodeima) edulis, Holothuria (Metriatyla) lessoni, Holothuria (Merten-siothuria) leucospilota, and Holothuria (Metriatyla) scabra were the 5 timun laut species that are grouped under the family Holothuriidae. The outcomes of this study can be utilised by enforcement agencies to monitor and overcome the issues of species substitution and product mislabelling of processed sea cucumber products in Malaysian markets.

A Scientist’s Walk Through Hanoi Old Quarter Houses

Among these, the recent work by Vuong et al. (2019) adds a unique view of the architectural features of Hanoi’s ancient townhouses as these features are viewed as dependent and independent variables. The study titled ‘Cultural evolution in Vietnam’s early 20th century: A Bayesian network analysis of Hanoi Franco-Chinese house designs’ aims to find traces of cultural evolution in the early 20th century in Vietnam and highlight the most notable elements that affect the Vietnamese people’s perception of cultural evolutions. The most striking feature of this study is the methodology employed to analyze and interpret the data. This study is the first among research conducted on the architecture of Hanoi ancient townhouses to employ the Bayesian network analysis method.

Study on data fuzzy breakpoint detection in massive dynamic data flow

The current method obtains the frequency of occurrence of abnormal data detected in the adjacent regions through reading between the sensor and the adjacent conversion data, and uses the frequency of occurrence of abnormal data to describe the spatial correlation, according to readings of sensor data using the Bayesian analysis method of sensor to determine whether the sensor is abnormal. But this method has the problem of low detection accuracy. For this reason, this paper proposes a method to detect the fuzzy breakpoint of data in the massive dynamic data flow. Firstly, this method used the amplitude difference method to determine the abnormal data amplitude and the discrete point difference of data fuzzy breakpoint, and then used the wavelet transform to extract the features of inflection point of the data fuzzy breakpoint. Combined with the features of inflection point of the extracted data fuzzy breakpoint, we carried out the support vector machine classification, and detected the data fuzzy breakpoints in the massive dynamic data flow. Experimental results show that the proposed method can effectively improve the accuracy of fuzzy breakpoint detection.

Study on data fuzzy breakpoint detection in massive dynamic data flow

The current method obtains the frequency of occurrence of abnormal data detected in the adjacent regions through reading between the sensor and the adjacent conversion data, and uses the frequency of occurrence of abnormal data to describe the spatial correlation, according to readings of sensor data using the Bayesian analysis method of sensor to determine whether the sensor is abnormal. But this method has the problem of low detection accuracy. For this reason, this paper proposes a method to detect the fuzzy breakpoint of data in the massive dynamic data flow. Firstly, this method used the amplitude difference method to determine the abnormal data amplitude and the discrete point difference of data fuzzy breakpoint, and then used the wavelet transform to extract the features of inflection point of the data fuzzy breakpoint. Combined with the features of inflection point of the extracted data fuzzy breakpoint, we carried out the support vector machine classification, and detected the data fuzzy breakpoints in the massive dynamic data flow. Experimental results show that the proposed method can effectively improve the accuracy of fuzzy breakpoint detection.

Development of a Maximum Entropy-Archimedean Copula-Based Bayesian Network Method for Streamflow Frequency Analysis—A Case Study of the Kaidu River Basin, China

Frequency analysis of streamflow is critical for water-resources system planning, water conservancy projects and the mitigation of hydrological extremes events. In this study, a maximum entropy-Archimedean copula-based Bayesian network (MECBN) method has been proposed for frequency analysis of monthly streamflow in the Kaidu River Basin, which integrates the maximum entropy-Archimedean copula (MEAC) and Bayesian network methods into a general framework. MECBN is effective for representing the uncertainties that exist in model representation, preserving the distributional characteristics of streamflow records and addressing the correlation structure between streamflow pairs. Application to the Kaidu River Basin shows a good performance of MECBN in describing the historical data of this basin in China. The results indicate that the interactions between two adjacent monthly streamflow pairs are non-linear. There is upper tail dependence between monthly streamflow pairs. The dependence coefficients including Spearman’s rho, Kendall’s tau, and the upper tail dependence coefficient are in inverse proportion of monthly streamflow values in the Kaidu River Basin, due to the fact that other factors (i.e., rainfall, snow melting, evapotranspiration rate and requirement of water use) provide more contributions to the streamflow in the flooding season. These findings can be used for providing vital information in the prevention and control of hydrological extremes and to further water resources planning in Kaidu River Basin.