Bayesian Inference Approach Research Articles

Bayesian survival analysis with INLA.

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.

A New Fuzzy Bayesian Inference Approach for Risk Assessments

Bayesian network (BN) inference is an important statistical tool with additive symmetry. However, BN inference cannot deal with the uncertain, fuzzy, random, and conflicting information from experts’ knowledge in the process of conducting a risk assessment. To tackle this issue, a new fuzzy BN inference approach for risk assessments was proposed based on cloud model (CM), interval type-2 fuzzy set (IT2 FS), interval type-2 fuzzy logic system (IT2 FLS), modified Dempster–Shafer (D-S) evidence theory (ET), and Latin hypercube sampling (LHS) methods along the following lines. Firstly, CM was integrated into IT2 FS, and CM-based IT2 FS (CM-IT2 FS) was defined in the IT2 FLS. Secondly, modified D-S ET was utilized to determine the CM-IT2 FS-based a priori probabilities, and the CM-IT2 FS-based BN model was established. Thirdly, the CM-IT2 FS-based a priori probabilities were reduced to the CM-IT1 FS-based ones using a type reducer in the IT2 FLS, LHS was applied to propose a new fuzzy BN inference algorithm, and then, the new algorithm was used in a typical case to perform the fuzzy BN positive inference for risk prediction and the fuzzy BN reverse inference for risk sensitivity analysis. Finally, the BN inference results were analyzed using the proposed algorithm and the two common BN inference algorithms, and the effectiveness of the proposed approach was validated. It can be concluded that the proposed approach was both accurate and promising.

IsoBayes: a Bayesian approach for single-isoform proteomics inference.

Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: i) infer the presence/absence of each protein isoform (via a posterior probability), ii) estimate its abundance (and credible interval), and iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

Identification of User Requirements and their Influencing Factors Based on Online Reviews and Operational Data

It is crucial for enterprises to clearly identify user needs during the process of formulating product design improvement plans. Therefore, it is essential to comprehensively and accurately identify user needs, explore the reasons behind the emergence of these needs, and incorporate user opinions into the process of product design improvement. A method is proposed to comprehensively and accurately capture user requirements and address the challenge of identifying the underlying causes of user requirements. This method utilizes online comments and operational data to identify user requirements and their influencing factors. First, text sentiment analysis techniques are employed to quantify the importance and performance values of product feature topics. Second, we construct a quadrant model to identify product features requiring improvement, and the original negative comments related to these features are traced. However, the quadrant model alone is insufficient to reflect specific product issues that users are concerned about. Therefore, a functional structure model based on product issues is designed to filter and identify factors that influence user requirements using operational data. Finally, a Bayesian network inference approach is utilized to identify the key influencing factors on user requirements, enabling analysis of the causes behind user requirements and the proposal of product design improvement strategies. The feasibility and effectiveness of the proposed method are validated through experiments conducted on heavy-duty truck data. By analyzing the original negative comments related to the power characteristics, specific user demands regarding the insufficient power of the product were identified, such as “obviously insufficient power when climbing slopes” and other issues. Based on the vehicle power system functional structure model, combined with expert knowledge and operational data, factors related to the state of parts and user behavior that may affect “insufficient vehicle power” were identified. Based on the analysis results, suggestions were made to improve the engine intake air temperature control strategy and to enhance vehicle performance by promoting correct user behavior through informational campaigns.

Confinement energy landscape classification reveals membrane receptor nano-organization mechanisms

The cell membrane organization has an essential functional role through the control of membrane receptor confinement in micro- or nanodomains. Several mechanisms have been proposed to account for these properties, although some features have remained controversial, notably the nature, size, and stability of cholesterol- and sphingolipid-rich domains or lipid rafts. Here, we probed the effective energy landscape acting on single-nanoparticle-labeled membrane receptors confined in raft nanodomains— epidermal growth factor receptor (EGFR), Clostridium perfringens ε-toxin receptor (CPεTR), and Clostridium septicum α-toxin receptor (CSαTR)—and compared it with hop-diffusing transferrin receptors. By establishing a new analysis pipeline combining Bayesian inference, decision trees, and clustering approaches, we systematically classified single-protein trajectories according to the type of effective confining energy landscape. This revealed the existence of only two distinct organization modalities: confinement in a quadratic energy landscape for EGFR, CPεTR, and CSαTR (A), and free diffusion in confinement domains resulting from the steric hindrance due to F-actin barriers for transferrin receptor (B).The further characterization of effective confinement energy landscapes by Bayesian inference revealed the role of interactions with the domain environment in cholesterol- and sphingolipid-rich domains with (EGFR) or without (CPεTR and CSαTR) interactions with F-actin to regulate the confinement energy depth. These two distinct mechanisms result in the same organization type (A). We revealed that the apparent domain sizes for these receptor trajectories resulted from Brownian exploration of the energy landscape in a steady-state-like regime at a common effective temperature, independently of the underlying molecular mechanisms. These results highlight that confinement domains may be adequately described as interaction hotspots rather than rafts with abrupt domain boundaries. Altogether, these results support a new model for functional receptor confinement in membrane nanodomains and pave the way to the constitution of an atlas of membrane protein organization.

Source localization for neutron imaging systems using convolutional neural networks.

The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium-tritium shots.

Bayesian Variable Shrinkage and Selection in Compositional Data Regression: Application to Oral Microbiome

Microbiome studies generate multivariate compositional responses, such as taxa counts, which are strictly non-negative, bounded, residing within a simplex, and subject to unit-sum constraint. In presence of covariates (which can be moderate to high dimensional), they are popularly modeled via the Dirichlet-Multinomial (D-M) regression framework. In this paper, we consider a Bayesian approach for estimation and inference under a D-M compositional framework, and present a comparative evaluation of some state-of-the-art continuous shrinkage priors for efficient variable selection to identify the most significant associations between available covariates, and taxonomic abundance. Specifically, we compare the performances of the horseshoe and horseshoe+ priors (with the benchmark Bayesian lasso), utilizing Hamiltonian Monte Carlo techniques for posterior sampling, and generating posterior credible intervals. Our simulation studies using synthetic data demonstrate excellent recovery and estimation accuracy of sparse parameter regime by the continuous shrinkage priors. We further illustrate our method via application to a motivating oral microbiome data generated from the NYC-Hanes study. RStan implementation of our method is made available at the GitHub link: (https://github.com/dattahub/compshrink).

Bayesian inference-based estimation of hourly primary and secondary organic carbon in suburban Hong Kong: multi-temporal-scale variations and evolution characteristics during PM2.5 episodes

Abstract. Observation-based data of primary and secondary organic carbon in ambient particulate matter (PM) are essential for model evaluation, climate and air quality research, health effect assessments, and mitigation policy development. Since there are no direct measurement tools available to quantify primary organic (POC) and secondary organic carbon (SOC) as separate quantities, their estimation relies on inference approaches using relevant measurable PM constituents. In this study, we measured hourly carbonaceous components and major ions in PM2.5 for a year and a half in suburban Hong Kong from July 2020 to December 2021. We differentiated POC and SOC using a novel Bayesian inference approach. The hourly POC and SOC data allowed us to examine temporal characteristics varying from diurnal and weekly patterns to seasonal variations, as well as their evolution characteristics during individual PM2.5 episodes. A total of 65 city-wide PM2.5 episodes were identified throughout the entire study period, with SOC contributions during individual episodes varying from 10 % to 66 %. In summertime typhoon episodes, elevated SOC levels were observed during daytime hours, and high temperature and NOx levels were identified as significant factors contributing to episodic SOC formation. Winter haze episodes exhibited high SOC levels, likely due to persistent influences from regional transport originating from the northern region to the sampling site. Enhanced SOC formation was observed with increase in the nocturnal NO3 radical (indicated by the surrogate quantity of [NO2][O3]) and under conditions characterized by high water content and strong acidity. These results suggest that both NO3 chemistry and acid-catalyzed aqueous-phase reactions likely make notable contributions to SOC formation during winter haze episodes. The methodology employed in this study for estimating POC and SOC provides practical guidance for other locations with similar monitoring capabilities in place. The availability of hourly POC and SOC data is invaluable for evaluating and improving atmospheric models, as well as understanding the evolution processes of PM pollution episodes. This, in turn, leads to more accurate model predictions and a better understanding of the contributing sources and processes.

Sequential Bayesian Phylogenetic Inference.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different node-calibration strategies and show that divergence-time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Gibbs sampler approach for objective Bayesian inference in elliptical multivariate meta-analysis random effects model

Bayesian inference procedures for the parameters of the multivariate random effects model are derived under the assumption of an elliptically contoured distribution when the Berger and Bernardo reference and the Jeffreys priors are assigned to the model parameters. A new numerical algorithm for drawing samples from the posterior distribution is developed, which is based on the hybrid Gibbs sampler. The new approach is compared to the two Metropolis-Hastings algorithms previously derived in the literature via an extensive simulation study. The findings are applied to a Bayesian multivariate meta-analysis, conducted using the results of ten studies on the effectiveness of a treatment for hypertension. The analysis investigates the treatment effects on systolic and diastolic blood pressure. The second empirical illustration deals with measurement data from the CCAUV.V-K1 key comparison, aiming to compare measurement results of sinusoidal linear accelerometers at four frequencies.

Two-stage stochastic model updating approach based on Bayesian framework and hybrid multi-population migrant genetic–Metropolis–Hastings algorithm for heavy-haul railway bridges

This study proposes a stochastic model updating approach using an improved multi-population migrant genetic algorithm (MMGA) with the Metropolis–Hastings (MH) algorithm based on the Bayesian inference approach. To enhance the search capacity of the genetic algorithm (GA), the MMGA introduces heuristic: the promotion and elimination mechanisms of urban population migration. The stochastic model updating approach includes two stages. The optimal values identified by the MMGA without considering the parameter uncertainties in the first stage are used as the initial values for the MH algorithm to accelerate Bayesian inference for uncertain parameters in the second stage. A practical application, the Yellow River Bridge with a heavy-haul railway line, demonstrates that the proposed approach offers an increased probability of gaining the global optimum, and predictions of displacements and frequencies provided by the updated finite element model are comparable to those obtained from the field tests.

A Bayesian inference approach to extract circuit model parameters and analyze photovoltaic degradation from power production data

Power production data can be a valuable resource to analyze photovoltaic (PV) performance without the need for field surveys. Recent work has demonstrated the exciting possibility of leveraging this data to extract circuit model parameters and current-voltage properties of a PV system, but further development is needed to bolster these initial findings. Here, instead of using a classical optimization approach, we switch to the Bayesian framework to solve this complex multi-solution problem. This allows us to construct probability distributions over the model parameters, get a comprehensive picture of the solution space, and quantify prediction uncertainty. As a result, we can define confidence intervals for the system’s electrical properties and consistently track their daily evolution. Our results are validated with laboratory measurements for five silicon and thin-film modules, and our scalable approach works with on-site as well as online weather data, which opens new prospects for remote PV monitoring, modeling, and degradation analysis for real-life applications.

A molecular phylogeny of Garuleum (Calenduleae, Asteraceae) in southern Africa

We present the first complete molecular phylogeny for the genus Garuleum (Calenduleae, Asteraceae), which is endemic to South Africa and Namibia. To investigate the phylogenetic relationships, sequence data from plastid trnL-trnF and nuclear ITS were analysed with Parsimony and Bayesian Inference approaches. The genus is resolved as monophyletic and G. bipinnatum is shown as the first species to diverge from the group. Four well to moderately supported subclades were identified where the species distribution ranges within a subclade were distinct. The wide distribution of G. bipinnatum was ascribed to its heterocarpic fruit, and the distribution of the genus was possibly influenced by water availability and elevation. These results confirmed the previous molecular work in which some Garuleum species were included.

Bayesian Maximal Reliability Evaluation Using Latent Variable Modeling

ABSTRACT We discuss an application of Bayesian factor analysis for estimation of the optimal linear combination and associated maximal reliability of a multi-component measuring instrument. The described procedure yields point and credibility interval estimates of this reliability coefficient, which are readily obtained in educational and behavioral measurement research. In addition, the outlined method permits evaluation of the gain in measurement consistency resulting from utilizing the maximal reliability coefficient instead of the traditionally used overall sum score reliability. The discussed Bayesian inference approach is applicable with widely available software in empirical studies, and is illustrated using a data example.

TCTAP A-002 Exploring the Success Rate in CTO PCI: Bayesian Approach for Preprocedural Inference and Intraprocedural Revising

TCTAP A-002 Exploring the Success Rate in CTO PCI: Bayesian Approach for Preprocedural Inference and Intraprocedural Revising

Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

AbstractThe functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.

Scalable and rapid building damage detection after hurricane Ian using causal Bayesian networks and InSAR imagery

Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies provide large-scale optical or Interferometric Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous event, which can be readily used to conduct rapid building damage assessment. Compared to optical satellite imageries, the Synthetic Aperture Radar can penetrate cloud cover and provide more complete spatial coverage of damaged zone in various weather conditions. However, these InSAR imageries often contain highly noisy and mixed signals induced by co-occurring or co-located building damage, flood, flood/wind-induced vegetation changes, as well as anthropogenic activities, making it challenging to extract accurate building damage information. In this paper, we introduced a causality-informed Bayesian network inference approach for rapid post-hurricane building damage detection from InSAR imagery. This approach encoded complex causal dependencies among wind, flood, building damage, and InSAR imagery using a holistic causal Bayesian network. Based on the causal Bayesian network, we further jointly inferred the large-scale unobserved building damage by fusing the information from InSAR imagery with prior physical models of flood and wind, without the need for ground truth labels. Furthermore, we validated our estimation results in a real-world devastating hurricane—the 2022 Hurricane Ian. We gathered and annotated building damage ground truth data in Lee County, Florida, and compared the introduced method’s estimation results with the ground truth and also benchmarked it against state-of-the-art models to assess the effectiveness of our proposed method. The results show that our method advances building damage assessment after hurricanes by accurately reflecting the complex dynamics between wind and flood impacts. Notably, it achieves this without the need for a ground truth label, which is a substantial step forward from traditional methods. Our model registers a 22.6% increase in the Area Under the Curve (AUC) and a 46.29% enhancement in the True Positive Rate (TPR). Moreover, it expedites the detection of building damage, cutting down processing times by up to 83.8%. These improvements mark a considerable leap in efficiency, demonstrating our method’s ability to streamline the assessment process markedly over conventional methods.

Non-informative Bayesian estimation in dispersion models

The estimation of parameters for a distribution function is a significant and prominent field within statistical inference. This particular problem holds great relevance in various domains, including industries, stock markets, image processing, and reliability studies. There are two recognized approaches to estimation: point estimation and interval estimation, also known as confidence intervals. In this study, our primary focus lies in the point estimation of parameters associated with an exponential dispersion distribution function. In this process, we consider one of the parameters as a random variable that requires estimation. To tackle this, we adopt a Bayesian inference approach utilizing a one-parameter dispersion distribution. We explore non-informative priors, such as uniform and Jeffrey's priors, and provide evidence of the effectiveness of our method through simulation studies.

Using spherical indentation to determine creep behavior with considering empirical friction coefficient

Indentation testing is a common technique for characterizing the mechanical properties of materials. When it comes to assessing the yield behavior of metals, conical indentation is often favored due to its ability to induce significant plastic deformation at relatively shallow indentation depths. On the other hand, spherical indentation is typically chosen for evaluating metal creep behavior, as it helps minimize the influence of plastic deformation on creep measurements. In spherical indentation tests, the friction at the contact interface is particularly crucial, given the larger contact area and more pronounced slip zones. However, accurately determining the friction coefficient at the contact interface is a complex multi-scale challenge, with the exact coefficient varying based on specific testing conditions and surface treatments. Consequently, empirical friction coefficients are frequently employed, often without thorough justification. In this work, we proposed Bayesian inference method for obtaining the creep constitutive behavior of alloys through spherical indentation tests. Our model considers not only the complexity of creep law, but also the indentation friction, which is unable to consider in most spherical indentation tests with traditional treatment. By using experimental data of spherical indentation tests on 310S stainless steel and pure nickel alloy from literature, present study demonstrates the effectiveness of the Bayesian inference approach, with the inferred constitutive models exhibiting well agreement with uniaxial creep behavior. Furthermore, the method considers the friction coefficient as an extra factor in inferring creep constitutive behavior from indentation tests.

Complete mitochondrial genome of Guigarracailaoensis Wang, Chen & Zheng, 2022 (Cypriniformes, Cyprinidae) and its phylogenetic implications.

Guigarracailaoensis is a member of family Cyprinidae, subfamily Labeoninae (Cypriniformes) which was recently discovered in southwestern China. Following its initial description, additional information on this species has remained notably scarce. In the current study, we assemble the complete mitochondrial genome (mitogenome) of G.cailaoensis using the Illumina sequencing platform. The mitogenome is identified as a circular, double-stranded DNA sequence of 16,593 base pairs, encompassing 13 protein-coding genes (PCGs), 22 transfer RNA genes, two ribosomal RNA genes, and a putative control region. Maximum-likelihood and Bayesian-inference approaches were used to construct phylogenetic trees for three datasets: (i) PCG sequences of the complete mitogenome (dataset 1); (ii) PCG sequences of the complete mitogenome combined with nuclear DNA (ncDNA) (Rag1) sequence (dataset 2); and (iii) ncDNA (Rag1) sequences (dataset 3). Phylogenetic analyses position G.cailaoensis as a sister taxon to the lineage consisting of Paraqianlabeolineatus Zhao, Sullivan, Zhang & Peng, 2014 and Pseudogyrinocheilusprochilus Fang, 1933 in dataset 1, and to Pseudogyrinocheilusprochilus in dataset 2, species lacking an oral disc on the lower lip. However, G.cailaoensis showed a close relationship to the lineage consisting of Discogobio and Discocheilus in dataset 3, species possessing an oral disc on the lower lip. Nonetheless, a variety of species with an oral disc on the lower lip are clustered into different lineages across the three datasets that may indicate that the development of the oral disc is homoplastic within the subfamily Labeoninae. The outcomes of this study have the potential to support conservation efforts for this species and to enrich our understanding of genetic resources in the area.