Hierarchical Inference Research Articles

On reliability challenges of repairable systems using hierarchical bayesian inference and maximum likelihood estimation

Failure modelling and reliability assessment of repairable systems has been receiving a great deal of attention due to its pivotal role in risk and safety management of process industries. Meanwhile, the level of uncertainty that comes with characterizing the parameters of reliability models require a sound parameter estimator tool. For the purpose of comparison and cross-verification, this paper aims at identifying the most efficient and minimal variance parameter estimator. Hierarchical Bayesian modelling (HBM) and Maximum Likelihood Estimation (MLE) approaches are applied to investigate the effect of utilizing observed data on inter-arrival failure time modelling. A case study of Natural Gas Regulating and Metering Stations in Italy has been considered to illustrate the application of proposed framework. The results highlight that relaxing the renewal process assumption and taking the time dependency of the observed data into account will result in more precise failure models. The outcomes of this study can help asset managers to find the optimum approach to reliability assessment of repairable systems.

Auditory Perceptual History Is Propagated through Alpha Oscillations.

SummaryPerception is a proactive, “predictive” process, in which the brain relies, at least in part, on accumulated experience to make best guesses about the world to test against sensory data, updating the guesses as new experience is acquired. Using novel behavioral methods, the present study demonstrates the role of alpha rhythms in communicating past perceptual experience. Participants were required to discriminate the ear of origin of brief sinusoidal tones that were presented monaurally at random times within a burst of uncorrelated dichotic white noise masks. Performance was not constant but varied with delay after noise onset in an oscillatory manner at about 9 Hz (alpha rhythm). Importantly, oscillations occurred only for trials preceded by a target tone to the same ear, either on the previous trial or two trials back. These results suggest that communication of perceptual history generates neural oscillations within specific perceptual circuits, strongly implicating behavioral oscillations in predictive perception and with formation of working memory.

An Adaptive Bayesian Parameter Estimation of a Synchronous Generator Under Gross Errors

Polynomial-chaos-expansion-based surrogate models have recently been advocated in the literature for power system dynamic parameter estimation. Regarding the estimation of the uncertain generator parameters, a Bayesian inference framework has been proposed based on a polynomial-based reduced-order representation of the synchronous machines using assumed parameter values. Then, the non-Gaussian posterior probability distribution functions (pdfs) of these parameters are recovered through the stochastic sampling approach efficiently. However, facing very large parameter errors, the reliability of the surrogate model decreases, yielding biased estimation results. To overcome this problem, this article develops a hierarchical Bayesian inference framework that processes the measurements provided by phasor measurement units, while making use of multifidelity surrogates together with the importance sampling method. The latter allows us to estimate in an efficient manner the posterior pdfs of the uncertain parameters through the normalized weights of the prior samples. To improve the accuracy of the posterior pdfs, an adaptive procedure is further adopted in the importance sampling for the gradual evolution of its proposal functions. The new proposals assist in fine-tuning the sample space and thereby help to construct surrogates with higher fidelity. Through an iterative process, this approach is able to estimate accurately and efficiently non-Gaussian posterior pdfs of the uncertain generator parameters subject to gross errors.

An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks

The security of energy supplies requires the depletion of potential fault events in power transmission networks. To achieve this, sufficient lead time before the happening of a fault event is indispensable for preparing countermeasures. With this inspiration, this paper establishes the fuzzy inference with rare association rule learning system. This ensemble system is designed for the long-term prediction of the spatiotemporal distribution of such energy security weaknesses, that is, to predict when and where these events are more expected to appear. In this system, merely the environmental features rather than the electrical features are needed as inputs. All the selected input features are divided into discrete and continuous features, and are evaluated separately. For the discrete features, the rare association rule learning model is implemented so that the rarely distributed environmental elements are extracted and diagnosed specifically. The risk indices of each element on the overall reliability are worked out as well. For the continuous features, a hierarchical fuzzy inference system along with the rare association rule learning model is deployed to calculate the corresponding risk indices of all the elements. In the hierarchical fuzzy inference system, the probabilistic fuzzy risks are employed instead of the direct fuzzy risks. Then the relative weights of these two sides are optimized. At last, an empirical case based on a practical transmission network is conducted, and the flexibility and the robustness of the proposed system during real applications can be validated consequently.

Modeling the dynamics of Plasmodium falciparum gametocytes in humans during malaria infection.

Renewed efforts to eliminate malaria have highlighted the potential to interrupt human-to-mosquito transmission - a process mediated by gametocyte kinetics in human hosts. Here we study the in vivo dynamics of Plasmodium falciparum gametocytes by establishing a framework which incorporates improved measurements of parasitemia, a novel gametocyte dynamics model and model fitting using Bayesian hierarchical inference. We found that the model provides an excellent fit to the clinical data from 17 volunteers infected with P. falciparum (3D7 strain) and reliably predicts observed gametocytemia. We estimated the sexual commitment rate and gametocyte sequestration time to be 0.54% (95% credible interval: 0.30-1.00%) per asexual replication cycle and 8.39 (6.54-10.59) days respectively. We used the data-calibrated model to investigate human-to-mosquito transmissibility, providing a method to link within-human host infection kinetics to epidemiological-scale infection and transmission patterns.

Investigation of the origin of the anomalous microwave emission in Lambda Orionis

Abstract The anomalous microwave emission (AME) still lacks a conclusive explanation. This excess of emission, roughly between 10 and 50 GHz, tends to defy attempts to explain it as synchrotron or free–free emission. The overlap with frequencies important for cosmic microwave background explorations, combined with a strong correlation with interstellar dust, drive cross-disciplinary collaboration between interstellar medium and observational cosmology. The apparent relationship with dust has prompted a “spinning dust” hypothesis. The typical peak frequency range of the AME profile implicates spinning grains on the order of 1 nm. This points to polycyclic aromatic hydrocarbons (PAHs). We use data from the AKARI/Infrared Camera (IRC), due to its thorough PAH-band coverage, to compare AME from the Planck Collaboration astrophysical component separation product with infrared dust emission in the λ Orionis AME-prominent region. We look also at infrared dust emission from other mid-infrared and far-infrared bands. The results and discussion contained here apply to an angular scale of approximately 1°. We find that dust mass certainly correlates with AME, and that PAH-related emission in the AKARI/IRC 9 μm band correlates slightly more strongly. Using hierarchical Bayesian inference and full-dust spectral energy distribution (SED) modeling we argue that AME in λ Orionis correlates more strongly with PAH mass than with total dust mass, lending support for a spinning PAH hypothesis within this region. We emphasize that future efforts to understand AME should focus on individual regions, and a detailed comparison of the PAH features with the variation of the AME SED.

Inferring the 3D Shapes of Extremely Metal-poor Galaxies from Sets of Projected Shapes

The three-dimensional (3-D) shape of a galaxy inevitably is tied to how it has formed and evolved and to its dark matter halo. Local extremely metal-poor galaxies (XMPs; defined as having an average gas-phase metallicity < 0.1 solar) are important objects for understanding galaxy evolution largely because they appear to be caught in the act of accreting gas from the cosmic web, and their 3-D shape may reflect this. Here we report on the 3-D shape of XMPs as inferred from their observed projected minor-to-major axial ratios using a hierarchical Bayesian inference model, which determines the likely shape and orientation of each galaxy while simultaneously inferring the average shape and dispersion. We selected a sample of 149 XMPs and divided it into three sub-samples according to physical size and found that (1) the stellar component of XMPs of all sizes tends to be triaxial, with an intermediate axis approx 0.7 times the longest axis and that (2) smaller XMPs tend to be relatively thicker, with the shortest axis going from approx 0.15 times the longest axis for the large galaxies to approx 0.4 for the small galaxies. We provide the inferred 3-D shape and inclination of the individual XMPs in electronic format. We show that our results for the intermediate axis are not clouded by a selection effect against face-on XMPs. We discuss how an intermediate axis significantly smaller than the longest axis may be produced by several mechanisms, including lopsided gas accretion, non-axisymmetric star formation, or coupling with an elongated dark matter halo. Large relative thickness may reflect slow rotation, stellar feedback, or recent gas accretion.

Modeling attention during visual search with hierarchical Bayesian inference

Modeling attention during visual search with hierarchical Bayesian inference

Hierarchical inference as a normative account for serial dependence

Hierarchical inference as a normative account for serial dependence

Hard exudate detection in retinal fundus images using supervised learning

The patients with diabetes have a chance to develop diabetic retinopathy (DR) which affects to the eyes. DR can cause blindness if the patients do not control diabetes. The patients with DR will have an impairment of metabolism of glucose causing a high glucose level in blood vessel called hyperglycemia. It leads to abnormal blood vessel and ultimately results in leakage of blood or fluid like lipoproteins, which are deposited under macular edema called hard exudates. They are normally white or yellowish-white with margins. Hard exudates are often arranged in clumps or circinate rings and located in the outer layer of the retina. The aim of this research was to detect hard exudates by applying several image processing techniques and classify them by using supervised learning methods including support vector machines and some neural network approaches, i.e., multilayer perceptron (MLP) network, hierarchical adaptive neurofuzzy inference system (hierarchical ANFIS), and convolutional neuron networks. DIARETDB1 which contains 89 fundus images is exploited as a dataset for evaluation. Hard exudate candidates are extracted by morphological techniques and classified by the classifiers trained by extracted patches with the corresponding ground truths. The tenfold cross-validation is applied to assure the generalization of the results. The proposed method achieves the area under the curve (AUC) of 0.998 when the MLP network is applied. The AUCs for all four classifiers are more than 0.95. This shows that the combination of image processing techniques and suitable classifiers can perform very well in hard exudate detection problem.

A fuzzy inference approach to analysis of LPV-200 procedures influence on air traffic safety

Introducing new technical and organizational solutions in air transport requires demonstrating that the level of safety will not be reduced. The LPV-200 (Localizer Performance with Vertical Guidance) approach for landing procedures represent a great opportunity for development of small, poorly equipped airports, as they permit precise landing without costly investment in the ILS (Instrument Landing System). The aim of this paper is to assess the effects of the introduction of LPV-200 procedures for air traffic safety which was determined by the probability of a CFIT (Controlled Flight Into Terrain) accident – PoC. Factors affecting PoC are of a diverse nature, some of which are subjective and cannot be expressed precisely. Therefore, PoC assessment uses fuzzy logic methods, and more specifically hierarchical fuzzy inference systems, with a knowledge base obtained from experts. As a result of simulation experiments, PoC was determined for airports with various levels of navigational equipment. It was also found that the introduction of LPV-200 procedures allows the reduction of PoC, with the highest effect being achieved for the least equipped airports. Also, in the event of failure of the main approach assistance system ILS, the use of LPV-200 procedures allows maintaining PoC at the same or close to the basic value level. The results of our research indicate that the introduction of LPV-200 procedures is clearly positive for the commercial use of small, less equipped aerodromes. We have shown that thanks to employing LPV-200 procedures it is possible to keep the PoC at a level similar to typical commercial airports.

Basilisk: Bayesian hierarchical inference of the galaxy–halo connection using satellite kinematics – I. Method and validation

ABSTRACT We present a Bayesian hierarchical inference formalism (Basilisk) to constrain the galaxy–halo connection using satellite kinematics. Unlike traditional methods, Basilisk does not resort to stacking the kinematics of satellite galaxies in bins of central luminosity, and does not make use of summary statistics, such as satellite velocity dispersion. Rather, Basilisk leaves the data in its raw form and computes the corresponding likelihood. In addition, Basilisk can be applied to flux-limited, rather than volume-limited samples, greatly enhancing the quantity and dynamic range of the data. And finally, Basilisk is the only available method that simultaneously solves for halo mass and orbital anisotropy of the satellite galaxies, while properly accounting for scatter in the galaxy–halo connection. Basilisk uses the conditional luminosity function to model halo occupation statistics, and assumes that satellite galaxies are a relaxed tracer population of the host halo’s potential with kinematics that obey the spherical Jeans equation. We test and validate Basilisk using mocks of varying complexity, and demonstrate that it yields unbiased constraints on the galaxy–halo connection and at a precision that rivals galaxy–galaxy lensing. In particular, Basilisk accurately recovers the full PDF of the relation between halo mass and central galaxy luminosity, and simultaneously constrains the orbital anisotropy of the satellite galaxies. Basilisk ’s inference is not affected by potential velocity bias of the central galaxies, or by slight errors in the inferred, radial profile of satellite galaxies that arise as a consequence of interlopers and sample impurity.

Correction to: On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco)

Correction to: On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco)

Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data

The Global Ecosystem Dynamic Investigation (GEDI) mission has been successfully launched to the International Space Station (ISS) on December 5th, 2018. While the sampling pattern of GEDI (8 transects with about 600 m across-track spacing) is sufficient to provide accurate biomass maps at the mission's required gridded resolution of 1 km, it is of significant interest to fuse GEDI data with ancillary, wall-to-wall remote sensing data from other sensors to provide increased accuracy, potentially higher resolution, and to fill gaps caused by track spacing, clumping in the ISS orbital tracks, as well as from clouds. In this paper, we examine the utility of combining simulated GEDI lidar observations with acquisitions from DLR's TerraSAR-X/TanDEM-X (TDX) InSAR mission to improve forest biomass estimation at a moderate resolution of 1 km, and to produce biomass maps at a much finer resolution of one hectare. We statistically characterize uncertainties before and after fusing simulated GEDI observations with TDX data across three different biomes: a temperate mixed deciduous forest, a temperate and mountainous coniferous forest, and a tropical forest. We implement the Random Volume over Ground (RVoG) model to derive canopy heights from TDX, using GEDI observations to parameterize key variables in the model. Our uncertainty framework is based on application of what are known as hybrid and hierarchical model-based inference, which allows for the parametric estimation of the variance of biomass estimation using both sampled and modeled data. We found improved accuracies after applying our fusion methods, with uncertainties at 1 km resolution ranging from 11%–20% across the different study sites of the mean biomass from the use of GEDI data alone, compared to 7%–12% achieved after the fusion. At one hectare resolution, comparison of the fusion-derived biomass with field plots showed uncertainties associated with mean biomass predictions between 11%–27% across sites. Our results suggest that the use of ancillary data that are sensitive to biomass, such as heights from TDX, with observations of ecosystem structure from GEDI, has the potential to both improve accuracies and/or provide finer spatial resolution, than what is achievable through either mission by itself. We further demonstrate that the GEDI data themselves may be used to parameterize models that invert height from TDX, leading to more accurate height maps for use in biomass estimation. Lastly, our results illustrate the benefits of using parametric estimation theory to quantify uncertainties from both sampling and modeling errors that arise in the fusion of multi-modal remote sensing data at various spatial scales.

Using machine learning to characterize heart failure across the scales.

Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain [Formula: see text] of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies.

On combining information from multiple gravitational wave sources

In the coming years, advanced gravitational wave detectors will observe signals from a large number of compact binary coalescences. The majority of these signals will be relatively weak, making the precision measurement of subtle effects, such as deviations from general relativity, challenging in the individual events. However, many weak observations can be combined into precise inferences, if information from the individual signals is combined in an appropriate way. In this study we revisit common methods for combining multiple gravitational wave observations to test general relativity, namely (i) multiplying the individual likelihoods of beyond-general-relativity parameters and (ii) multiplying the Bayes Factor in favor of general relativity from each event. We discuss both methods and show that they make stringent assumptions about the modified theory of gravity they test. In particular, the former assumes that all events share the same beyond-general-relativity parameter, while the latter assumes that the theory of gravity has a new unrelated parameter for each detection. We show that each method can fail to detect deviations from general relativity when the modified theory being tested violates these assumptions. We argue that these two methods are the extreme limits of a more generic framework of hierarchical inference on hyperparameters that characterize the underlying distribution of single-event parameters. We illustrate our conclusions first using a simple model of Gaussian likelihoods, and also by applying parameter estimation techniques to a simulated dataset of gravitational waveforms in a model where the graviton is massive. We argue that combining information from multiple sources requires explicit assumptions that make the results inherently model-dependent.

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.

Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of our theory enables researchers to make inference on group-level parameters by performing HBI t-test.

Evolution of Angiosperm Pollen. 7. Nitrogen-fixing Clade

Nitrogen-fixing symbiosis in root nodules is known in only 10 families, which are distributed among a clade of four orders and delimited as the nitrogen-fixing clade. As the seventh in a series that examines pollen morphological distribution and evolution in the angiosperms, this paper focuses on pollen morphological character states of the nitrogen-fixing clade. To illustrate the palynological diversity of the clade, we first examined pollen grains from 26 species with light electron, scanning electron, and transmission electron microscopy. Second, we used a reduced data matrix from Li et al. (2015) to reconstruct a maximum likelihood tree and then optimized 18 pollen character states onto the tree using Fitch parsimony, maximum likelihood, and hierarchical Bayesian inference. Finally, 12 plesiomorphic states for the nitrogen-fixing clade were inferred unambiguously under all methods, and more than 40 clades (or lineages) at or above familial level were characterized by unambiguous pollen character state changes in at least one of the optimizations. We found a number of evolutionary trends for changes in pollen character states. These include increasing grain size, increasing aperture number accompanied by concomitant changes in aperture position (from equatorial to global) and aperture shape (from colpate to colporate), and increasing complexity of tectum ornamentation. There was a strong correlation between some pollen characters (prolate shape class, lobe outline in polar view, colpate ectoaperture, lalongate and lolongate endoaperture, absent supratectal element, reticulate tectum) and insect pollination, while other pollen characters—simple aperture structure, porate ectoaperture, circular endoaperture, present and gemmate or echinate supratectal element, and imperforate tectum—were strongly correlated with wind pollination. In addition, rugulate tectum was significantly correlated with shrub habit while larger pollen size was significantly correlated with vine habit; the helophytic habitat was significantly correlated with having two apertures. Our study provides rich evidence for the phylogenetic significance of pollen morphological diversity in the nitrogen-fixing clade.

On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco)

The main purpose of this study is to investigate the use of hierarchical fuzzy inference systems (HFISs) in expert-based landslide susceptibility mapping in a data-scarce region. Taounate-Ain Aicha and Tahar Souk regions in the central part of the Rif Mountains in Morocco were selected as the case study area. The research was performed in three main stages: (i) the landslide inventory of the region was produced and the conditioning factors were evaluated; (ii) the theoretical background for HFIS was introduced; and (iii) different types of structures and methods of HFIS were investigated in the construction of expert-based models; the landslide information was only used for validation of the expert-based models in this stage. Regarding the inference methods, the defuzzification-free hierarchical fuzzy system (DF-HFS) has not only the remarkable advantage of low cost of computation but also preservation of information. The most successful result was acquired from the model developed using HFISs, which was designed by considering a defuzzification-free hybrid structure with standard membership functions. This research is the first study in which HFISs are evaluated in expert-based landslide susceptibility mapping. HFISs and the rule-generation algorithm implemented in this study will allow fuzzy systems to be applied effectively not only in landslide susceptibility mapping but also in other geoscientific and geo-engineering solutions.

Retraction Note to: Dynamic stability enhancement of interconnected multi-source power systems using hierarchical ANFIS controller-TCSC based on multi-objective PSOs

Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic generation control (AGC). To alleviate the system oscillation resulting from such load changes, implementation of flexible AC transmission systems (FACTSs) can be considered as one of the practical and effective solutions. In this paper, a thyristor-controlled series compensator (TCSC), which is one series type of the FACTS family, is used to augment the overall dynamic performance of a multi-area multi-source interconnected power system. To this end, we have used a hierarchical adaptive neuro-fuzzy inference system controller-TCSC (HANFISC-TCSC) to abate the two important issues in multi-area interconnected power systems, i.e., low-frequency oscillations and tie-line power exchange deviations. For this purpose, a multi-objective optimization technique is inevitable. Multi-objective particle swarm optimization (MOPSO) has been chosen for this optimization problem, owing to its high performance in untangling non-linear objectives. The efficiency of the suggested HANFISC-TCSC has been precisely evaluated and compared with that of the conventional MOPSO-TCSC in two different multi-area interconnected power systems, i.e., two-area hydro-thermal-diesel and three-area hydro-thermal power systems. The simulation results obtained from both power systems have transparently certified the high performance of HANFISC-TCSC compared to the conventional MOPSO-TCSC.