Hierarchical Inference Research Articles

Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity.

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, neurobiological interpretations of predictive coding view perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs), and disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a hierarchical Bayesian model to single-trial EEG data from healthy human volunteers of either sex who received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207 ms poststimulus), while high-level PEs (about transition probability) are reflected by later components (152-199 and 215-277 ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts the inference on abstract statistical regularities. Our findings suggest that NMDAR dysfunction impairs hierarchical Bayesian inference about the world's statistical structure. Beyond the relevance of this finding for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential pathophysiological mechanisms.

The Issues and Challenges of Local Government Units in the Era of Population Ageing

So far, issues related to the effects of ageing have been studied mainly from the perspective of national policy and international analyses. The study fills the research gap in this area focusing on the impact of demographic changes on the policy of local government units (LGUs). In the first section, a literature review of the research conducted so far has been performed. The empirical part contains the results of the study that was carried out on a sample of 131 municipalities of the Małopolska Voivodeship. The results obtained were subject to statistical analysis, based upon elements of descriptive statistics, cluster analysis and hierarchical clustering (k-means algorithm, Ward’s method) and statistical inference (Chi-square tests). It was found that the most important social problems identified by municipalities include in particular: demographic processes (indicated as significant by 71% of municipalities), population migrations (76%), unemployment (88%), alcoholism (93%) and poverty (75%). On the other hand, the following problems are perceived to a lesser extent: deficits in the level of education (15%) and social capital (36%), as well as participation in public (31%) and cultural (34%) life. However, only a few municipalities, mainly ones that are larger and more urbanised, see the key importance of the intensity of the ageing process (33% of cities with powiat rights, 14% of urban municipalities and only 4% of rural municipalities). Studies also indicate that in the light of the diagnosis of current problems, the greatest challenges associated with an ageing population are related to the lack of adequate financial resources, a decrease in a municipality’s income, a deteriorating situation in the local labour market, reduced family care potential, increased health care costs and social aid. It was also noted that from the municipalities’ point of view, the consequences of an ageing population will not only have a financial dimension, but will also require a certain redefinition of the catalogue of tasks to be carried out.

Integrated nested Laplace approximation method for hierarchical Bayesian inference of spatial model with application to functional magnetic resonance imaging data

Analysis of functional magnetic resonance imaging (fMRI) data plays a crucial role in medical statistics. Many developed approaches have relied on the voxel-wise general linear model (GLM). Despite the popularity of classical GLM, this procedure has some drawbacks in modeling complex structure of fMRI data. Bayesian approaches have largely dealt with this issue. Due to the computational cost of Bayesian approach such as Monte Carlo Markov Chain (MCMC), approximate Bayesian methods including variational Bayes (VB) and Integrated Nested Laplacian Approximation (INLA) have been proposed. In this study, we use INLA for inferences of spatial hierarchical model. Moreover, temporal correlations of time series is addressed by pre-whitening approach. To detect regions of activation, we apply joint posterior distributions as opposed to marginal distributions. The joint posterior probability map (PPM) is computed based on excursions set theory, and multiple comparisons problem is properly corrected. The performance of the proposed Bayesian approach is evaluated via simulation studies and a real task fMRI dataset. The results are compared with VB and classical GLM. The Bayesian approach based on joint PPM leads to more precise results by controlling false positive rate. The posterior mean from INLA fairly agree with the posterior mean from VB, however VB underestimates posterior variance.

Reliability and importance analysis of uncertain system with common cause failures based on survival signature

Redundant design has become the commonly used technique for ensuring the reliability of complex systems, which calls for great concern to common cause failure problems in such systems. Incomplete data in combination with vague judgments from experts introduce imprecision and epistemic uncertainties in the performance characterization of components. These issues need to be taken into account for assessing the system reliability. In this paper, a comprehensive reliability assessment method is presented by adopting the concept of survival signature to estimate the reliability of complex systems with multiple types of components. Particular attention is devoted to common cause failures (CCFs), which are modeled and quantified by decomposed partial α-decomposition method. Uncertainties caused by incomplete data for CCF events are reduced by hierarchical Bayesian inference. The component importance measure is enhanced to assess the importance of various possible CCF scenarios and to identify their potential impact on system reliability. The presented method is used to analyze the reliability of a dual-axis pointing mechanism for communication satellite, which is a commonly used satellite antenna control mechanism. The engineering application demonstrates the effectiveness of the method.

Hierarchical structure correlation inference for pose estimation

Human pose estimation is a challenging and important basic subject in computer vision. At present, mainstream methods have achieved remarkable progress through the powerful representation capabilities of convolution neural networks. However, these methods don’t pay enough attention to the correlation of the structure of each part of the human body in pose estimation. To solve this problem, the present paper proposes the hierarchical structure correlation inference algorithm, composed of correlation inference modules, which increases the ability of the network to infer the correlation information of each part of the body. Moreover, this algorithm promotes the understanding of the human body structure. The proposed algorithm is effectively verified on the MPII Human Pose Dataset and Leeds Sport Pose Dataset, and the experiment shows that our results are better than current mainstream algorithms.

Close Binary Companions to APOGEE DR16 Stars: 20,000 Binary-star Systems Across the Color–Magnitude Diagram

Abstract Many problems in contemporary astrophysics—from understanding the formation of black holes to untangling the chemical evolution of galaxies—rely on knowledge about binary stars. This, in turn, depends on the discovery and characterization of binary companions for large numbers of different kinds of stars in different chemical and dynamical environments. Current stellar spectroscopic surveys observe hundreds of thousands to millions of stars with (typically) few observational epochs, which allows for binary discovery but makes orbital characterization challenging. We use a custom Monte Carlo sampler (The Joker) to perform discovery and characterization of binary systems through radial velocities, in the regime of sparse, noisy, and poorly sampled multi-epoch data. We use it to generate posterior samplings in Keplerian parameters for 232,495 sources released in APOGEE Data Release 16. Our final catalog contains 19,635 high-confidence close-binary (P ≲ few years, a ≲ few ) systems that show interesting relationships between binary occurrence rate and location in the color–magnitude diagram. We find notable faint companions at high masses (black hole candidates), at low masses (substellar candidates), and at very close separations (mass-transfer candidates). We also use the posterior samplings in a (toy) hierarchical inference to measure the long-period binary-star eccentricity distribution. We release the full set of posterior samplings for the entire parent sample of 232,495 stars. This set of samplings involves no heuristic “discovery” threshold and therefore can be used for myriad statistical purposes, including hierarchical inferences about binary-star populations and subthreshold searches.

Multisensory Integration Develops Prior to Crossmodal Recalibration.

It has been hypothesized that crossmodal recalibration plays a crucial role for the development of multisensory integration capabilities [1]. To test the developmental trajectory of multisensory integration and crossmodal recalibration, we used a combined ventriloquist/ventriloquist aftereffect paradigm [2] in children aged 5-9 years. The ventriloquist effect (indicating multisensory integration), that is, the shift of auditory localization toward simultaneously presented but spatially discrepant visual stimuli, was larger in children than in adults, which was attributed to a lower auditory localization precision in the children. In fact, the size of the ventriloquist effect depended on the visual stimulus reliability in both children and adults. In all groups, the ventriloquist effect was best explained by a causal inference model. In contrast to their multisensory integration capabilities, 5-year-old children did not recalibrate. The immediate ventriloquist aftereffect (indicating recalibration after a single exposure to a spatially discrepant audio-visual stimulus) emerged in 6- to 7-year-old children, whereas the cumulative ventriloquist aftereffect (reflecting recalibration to the audio-visual spatial discrepancies over the complete experiment) was not observed before the age of 8 years. First, in contrast to common beliefs, the present results provide evidence that multisensory integration precedes rather than follows crossmodal recalibration during development. Second, we report developmental evidence for a dissociation of the processes involved in multisensory integration and immediate as well as cumulative recalibration. We speculate that multisensory integration is a prerequisite for crossmodal recalibration, because the multisensory percept, rather than unimodal cues, might comprise a crucial signal for the calibration of the sensory systems.

Gravitational waves or deconfined quarks: What causes the premature collapse of neutron stars born in short gamma-ray bursts?

We infer the collapse times of long-lived neutron stars into black holes using the X-ray afterglows of 18 short gamma-ray bursts. We then apply hierarchical inference to infer properties of the neutron star equation of state and dominant spin-down mechanism. We measure the maximum non-rotating neutron star mass $M_\mathrm{TOV} = 2.31 ^{+0.36}_{-0.21} M_{\odot}$ and constrain the fraction of remnants spinning down predominantly through gravitational-wave emission to $\eta = 0.69 ^{+0.21}_{-0.39}$ with $68 \%$ uncertainties. In principle, this method can determine the difference between hadronic and quark equation of states. In practice, however, the data is not yet informative with indications that these neutron stars do not have hadronic equation of states at the $1\sigma$ level. These inferences all depend on the underlying progenitor mass distribution for short gamma-ray bursts produced by binary neutron star mergers. The recently announced gravitational-wave detection of GW190425 suggests this underlying distribution is different from the locally-measured population of double neutron stars. We show that $M_\mathrm{TOV}$ and $\eta$ constraints depend on the fraction of binary mergers that form through a distribution consistent with the locally-measured population and a distribution that can explain GW190425. The more binaries that form from the latter distribution, the larger $M_\mathrm{TOV}$ needs to be to satisfy the X-ray observations. Our measurements above are marginalised over this unknown fraction. If instead, we assume GW190425 is not a binary neutron star merger, i.e the underlying mass distribution of double neutron stars is the same as observed locally, we measure $M_\mathrm{TOV} = 2.26 ^{+0.31}_{-0.17} M_{\odot}$.

Comparison of lower limb joint moment and power during turning gait between young and old adults using hierarchical Bayesian inference

Age-related differences in lower limb joint moment (JM), and joint power (JP) during turning remain unclear. The present study investigated age-related differences in lower limb JM and JP during turning between young adults (YAs) and old adults (OAs). We introduced the hierarchical Bayesian inference for comparing and identifying differences in JM, angular velocity(ω), and JP at each stance phase in the two age groups. This study included 16 healthy YAs and 16 healthy OAs (8 men and 8 women in each group). Participants performed 90° step turns to the right at a self-selected natural speed. On comparing the age groups, during 90° step turning, the OA group exhibited larger extention hip JM and JP to control (brake) the upper body in the sagittal plane, exhibited larger abductor moment in each lower limb joint for preventing the body from leaning in the frontal plane during the mid-stance phase, and exhibited larger hip JP and ω and smaller ankle JM in the transverse plane to rotate the body during the mid-stance phase. Our findings suggested that the overall reliance on the hip joint to control body motion in each anatomical plane during step turning is higher in the OA group than in the YA group. In addition, the hierarchical Bayesian inference is useful for comparing the time courses of JM, ω, and JP.

Inter-industry wage inequality: persistent differences and turbulent equalisation

AbstractThe empirical stylised fact of persistent inter-industry wage differentials is an enduring challenge to economic theory. This paper applies the classical theory of ‘real competition’ to the turbulent dynamics of these inter-industrial wage differentials. Theoretically, we argue that competitive wage determination can be decomposed into equalising, dispersing and turbulently equalising factors. Empirically, we show graphically and econometrically for 31 US industries in 1987–2016 that wage differentials, like regulating profit rates, are governed by turbulent equalisation. Furthermore, we apply a fixed-effects OLS as well as a hierarchical Bayesian inference model and find that the link between regulating profit rates and wage differentials is positive, significant and robust.

IMPROVEMENT OF PROJECT RISK ASSESSMENT METHODS OF IMPLEMENTATION OF AUTOMATED INFORMATION COMPONENTS OF NON-COMMERCIAL ORGANIZATIONAL AND TECHNICAL SYSTEMS

The results of a study using the methodological apparatus of the theory of fuzzy logic and automation tools for analyzing input data for risk assessment of projects for the implementation of automated information components of organizational and technical systems are presented. Based on the model of logistics projects for motor transport units, the method for assessing the risks of projects implementing automated information components of non-commercial organizational and technical systems has been improved. To do this, let’s analyze the peculiarities of implementing ERP projects as commercial ones and investigate the specifics of the activities of state institutions, when successful tasks, and not economic indicators, lay the foundation for the assessment. It is considered that it is possible to formulate a system of risk assessment indicators for reducing the effectiveness of projects for implementing automated information systems in non-commercial organizational and technical systems. A meaningful interpretation of the fuzzy approach is carried out regarding the formalization of the risk assessment process for projects of automated information systems of public institutions. A tree of fuzzy inference is constructed based on the results of a study of the description of indicators and expert assessments on the risk assessment of the implementation of the project of such an automated information system. The improved method differs from the known ones by the use of hierarchical fuzzy inference, which makes it possible to quantify, reduce the time to evaluate project risks and improve the quality of decisions. An increase in the number of input variables leads to an increase in complexity (an increase in the number of rules) for constructing a fuzzy inference system. The construction of a hierarchical system of fuzzy inference and knowledge bases can reduce complexity (the number of rules). The development of a software module based on the algorithm of the method as part of corporate automated information systems of non-commercial organizational and technical systems will reduce the time for risk assessment of projects for the implementation of automated information systems.

Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus).

Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distance sampling detection functions and subsequently used as an offset when fitting a density-habitat model. Although variance propagation techniques have recently become available for the Two-Stage Method, most previous applications have not propagated detection probability uncertainty into final density estimates. In this paper, we describe an alternative approach for fitting DSMs based on Bayesian hierarchical inference (hereafter referred to as the Bayesian Method), which is a natural framework for simultaneously propagating multiple sources of uncertainty into final estimates. Our framework includes (1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, (2) an informed prior for the probability a group of animals is at the surface and available for detection (i.e. surface availability) (3) a density-habitat model incorporating spatial smoothers and (4) a flexible compound Poisson-gamma model for count data that incorporates overdispersion and zero-inflation. We evaluate our method and compare its performance to the Two-Stage Method with simulations and an application to line transect data of fin whales (Balaenoptera physalus) off the east coast of the USA. Simulations showed that both methods had low bias (<1.5%) and confidence interval coverage close to the nominal 95% rate when variance was propagated from the first step. Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods; however, the coefficient of variation of the final abundance estimate more than doubled (0.14 vs 0.31) when detection variance was correctly propagated into final estimates. An analysis of the variance components demonstrated that overall detectability as well as surface availability contributed substantial amounts of variance in the final abundance estimates whereas uncertainty in mean group size contributed a negligible amount. Our method provides a Bayesian alternative to DSMs that incorporates much of the flexibility available in the Two-Stage Method. In addition, these results demonstrate the degree to which uncertainty can be underestimated if certain components of a DSM are assumed fixed.

Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software

Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software

Comments on: Hierarchical Inference for genome-wide association studies: a view on methodology with software by Paulo C. Rodrigues and Vanda M. Lourenço

Comments on: Hierarchical Inference for genome-wide association studies: a view on methodology with software by Paulo C. Rodrigues and Vanda M. Lourenço

Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software by F. Frommlet and M. G. Schimek

Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software by F. Frommlet and M. G. Schimek

Comments on: Hierarchical inference for genome-wide association studies by Jelle J. Goeman and Stefan Böhringer

Comments on: Hierarchical inference for genome-wide association studies by Jelle J. Goeman and Stefan Böhringer

Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software

Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software

Hierarchical inference for genome-wide association studies: a view on methodology with software

We provide a view on high-dimensional statistical inference for genome-wide association studies (GWAS). It is in part a review but covers also new developments for meta analysis with multiple studies and novel software in terms of an R-package hierinf. Inference and assessment of significance is based on very high-dimensional multivariate (generalized) linear models: in contrast to often used marginal approaches, this provides a step towards more causal-oriented inference.

Adaptive method of detecting traffic anomalies in high-speed multi-service communication networks

In the paper, an adaptive hybrid heuristic (behavioral) method for detecting small traffic anomalies in high-speed multiservice communication networks, which operates in real time, is proposed and investigated. The relevance of this study is determined by the fact that network security management processes in high-speed multiservice communication networks need to be implemented in a mode close to real-time mode, as well as identifying possible network security threats in the early stages of the implementation of possible network attacks. The proposed method and algorithm belong to the class of adaptive methods and algorithms with preliminary training. The average relative error in estimating the evaluated traffic parameters does not exceed 10%, which is sufficient for the implementation of operational network management tasks. Anomalies of the expectation of traffic intensity and its dispersion are identified if their valuesexceed the normal values by 15% or more, which makes it possible to detect possible network attacks in the early phases of their implementation, for example, at the stage of scanning ports and interfaces of the attacked system. The procedure for detecting anomalous traffic behavior is implemented based on the Mamdani’s method of hierarchical fuzzy logical inference. A study of the proposed method for detecting anomalous behavior of network traffic showed its high efficiency.

A Novel Construction and Inference Methodology of Belief Rule Base

The belief rule base model performs well in continuous systems but not in piecewise systems, and it suffers from a combinatorial explosion when there are a large number of model input referential parameters, resulting in a slow model optimization speed. Aiming at these problems, we do the following things. Firstly, different from the existing methods that improve model performance by improving optimization algorithm, we propose a distributed belief rule base model construction and inference methodology, which reduces the algorithm complexity of model construction and inference and improves the model accuracy by dividing the model into several small and independent subsets of belief rule base. Then we prove that the distributed belief rule base model can reduce the optimization algorithm complexity by using this methodology optimizes all subsets in parallel and independently during model construction, and reduce the inference cost of inactivated rules by adopting a hierarchical inference methodology. Then we prove that the proposed distributed belief rule base model performs well on both continuous and piecewise systems; also, construction efficiency and inference accuracy using the same optimization algorithm are higher than those of the traditional belief rule base model through experiments of nonlinear continuous function fitting and binary piecewise function fitting. Finally, we improve that the proposed distributed belief rule base model has a noticeable performance advantage in complex environments compared to the traditional model through the network situation prediction experiment and point out that the distributed belief rule base model is suitable in applications that require high real-time performance and high accuracy.