Bayesian Machine Learning Techniques Research Articles

Improvement and generalization of ABCD method with Bayesian inference

To find New Physics or to refine our knowledge of the Standard Model at the LHC is an enterprise that involves many factors, such as the capabilities and the performance of the accelerator and detectors, the use and exploitation of the available information, the design of search strategies and observables, as well as the proposal of new models. We focus on the use of the information and pour our effort in re-thinking the usual data-driven ABCD method to improve it and to generalize it using Bayesian Machine Learning techniques and tools. We propose that a dataset consisting of a signal and many backgrounds is well described through a mixture model. Signal, backgrounds and their relative fractions in the sample can be well extracted by exploiting the prior knowledge and the dependence between the different observables at the event-by-event level with Bayesian tools. We show how, in contrast to the ABCD method, one can take advantage of understanding some properties of the different backgrounds and of having more than two independent observables to measure in each event. In addition, instead of regions defined through hard cuts, the Bayesian framework uses the information of continuous distribution to obtain soft-assignments of the events which are statistically more robust. To compare both methods we use a toy problem inspired by pp\to hh\to b\bar b b \bar bpp→hh→bb‾bb‾, selecting a reduced and simplified number of processes and analysing the flavor of the four jets and the invariant mass of the jet-pairs, modeled with simplified distributions. Taking advantage of all this information, and starting from a combination of biased and agnostic priors, leads us to a very good posterior once we use the Bayesian framework to exploit the data and the mutual information of the observables at the event-by-event level. We show how, in this simplified model, the Bayesian framework outperforms the ABCD method sensitivity in obtaining the signal fraction in scenarios with 1% and 0.5% true signal fractions in the dataset. We also show that the method is robust against the absence of signal. We discuss potential prospects for taking this Bayesian data-driven paradigm into more realistic scenarios.

A Simulation-Based Model for Continuous Network Design Problem Using Bayesian Optimization

This paper investigates the continuous network design problem (CNDP) and proposes a simulation-based bi-level model and solution framework based on Bayesian optimization. In the bi-level model, the upper level minimizes the total system cost, and the lower level assigns traffic under an approximated dynamic equilibrium condition corresponding to the given network design strategy. A simulation tool integrated micro- and macro- traffic dynamics, namely SUMO, is employed to solve the lower-level problem. The embedded high-fidelity, non-linear understanding of traffic in the simulator oftentimes gains additional complexity due to the lack of tractable mathematical representation. Thus, a Bayesian machine learning technique is utilized to build the link between simulation and optimization. The proposed solution framework takes both the advantages of fine-grained simulations and the efficiency of surrogate-based optimization techniques. Moreover, lane width expansion is innovatively proposed as the decision variable of CNDP to bridge the gap between theory and practice. The relationship between link free-flow speed (FFS) and lane width is also explored based on real data and established to accurately calibrate the simulation input. For demonstrative purposes, numerical experiments on the optimal lane width expansion design were conducted in the inner city of Suzhou, China. The results show that with proper parameter settings, the proposed method is capable to find the global optimal solution within a very tight computational budget, which makes the simulation-based framework an encouraging option for policymakers to enhance transportation network performance.

Optimized pollard route deviation and route selection using Bayesian machine learning techniques in wireless sensor networks

Optimal route selection and circumventing the route deviation is essential in sensor transmission to reach the destination properly and to save energy in sensors. Wireless sensor networks (WSNs) play an indispensable role to achieve faster communication. Sensors are tiny devices which can store less power and need the power to be retained until final communication. The main need is to achieve routing of the sensors while performing the data transmission should be taken care. Optimal routing technique is necessitated to transfer data from sensors in the clusters and to the central station. The main focus is to dwindle the battery power consumption and increase the network life time. In this proposed work, the route deviation is pollard by Bayesian machine learning technique which uses the posterior distribution incrementally when new evidence is occurred. The approach calculates the conditional probability using the prior knowledge to determine the route deviation and optimal route. The methodology mainly focuses on parameters like, end-to-end delay, detection of route deviation, optimal route selection and network life time. The experimental results of proposed Optimal Pollard Route Deviation using Bayesian (OPDB) protocol focuses on the evaluation metrics of machine learning algorithm in terms of accuracy and error rate. The proposed algorithm is 28.5% better in minimizing the route deviation, 86.67% improved route selection, delay is very much minimized up to 07.12% and the 93.87% improved network life time compared with other routing algorithms . The route deviation detection is 14.5% improved, optimal route selection is improved by 31.84%, delay is minimized by 20.32% and network lifetime is increased by15.24% while using the OPDB algorithm.

A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data

BackgroundPrior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data when covariate and outcome data are missing at random (MAR). This approach however is computationally expensive, especially on large-scale datasets.MethodsWe propose an inference-based method, called RR-BART, which leverages the likelihood-based Bayesian machine learning technique, Bayesian additive regression trees, and uses Rubin’s rule to combine the estimates and variances of the variable importance measures on multiply imputed datasets for variable selection in the presence of MAR data. We conduct a representative simulation study to investigate the practical operating characteristics of RR-BART, and compare it with the bootstrap imputation based methods. We further demonstrate the methods via a case study of risk factors for 3-year incidence of metabolic syndrome among middle-aged women using data from the Study of Women’s Health Across the Nation (SWAN).ResultsThe simulation study suggests that even in complex conditions of nonlinearity and nonadditivity with a large percentage of missingness, RR-BART can reasonably recover both prediction and variable selection performances, achievable on the fully observed data. RR-BART provides the best performance that the bootstrap imputation based methods can achieve with the optimal selection threshold value. In addition, RR-BART demonstrates a substantially stronger ability of detecting discrete predictors. Furthermore, RR-BART offers substantial computational savings. When implemented on the SWAN data, RR-BART adds to the literature by selecting a set of predictors that had been less commonly identified as risk factors but had substantial biological justifications.ConclusionThe proposed variable selection method for MAR data, RR-BART, offers both computational efficiency and good operating characteristics and is utilitarian in large-scale healthcare database studies.

Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers.

Background Medication nonadherence represents a major burden on national health systems. According to the World Health Organization, increasing medication adherence may have a greater impact on public health than any improvement in specific medical treatments. More research is needed to better predict populations at risk of medication nonadherence. Objective To develop clinically informative, easy-to-interpret machine learning classifiers to predict people with psychiatric disorders at risk of medication nonadherence based on the syntactic and structural features of written posts on health forums. Methods All data were collected from posts between 2016 and 2021 on mental health forum, administered by Together 4 Change, a long-running not-for-profit organisation based in Oxford, UK. The original social media data were annotated using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC) system. Through applying multiple feature optimisation techniques, we developed a best-performing model using relevance vector machine (RVM) for the probabilistic prediction of medication nonadherence among online mental health forum discussants. Results The best-performing RVM model reached a mean AUC of 0.762, accuracy of 0.763, sensitivity of 0.779, and specificity of 0.742 on the testing dataset. It outperformed competing classifiers with more complex feature sets with statistically significant improvement in sensitivity and specificity, after adjusting the alpha levels with Benjamini–Hochberg correction procedure. Discussion. We used the forest plot of multiple logistic regression to explore the association between written post features in the best-performing RVM model and the binary outcome of medication adherence among online post contributors with psychiatric disorders. We found that increased quantities of 3 syntactic complexity features were negatively associated with psychiatric medication adherence: “dobj_stdev” (standard deviation of dependents per direct object of nonpronouns) (OR, 1.486, 95% CI, 1.202–1.838, P < 0.001), “cl_av_deps” (dependents per clause) (OR, 1.597, 95% CI, 1.202–2.122, P, 0.001), and “VP_T” (verb phrases per T-unit) (OR, 2.23, 95% CI, 1.211–4.104, P, 0.010). Finally, we illustrated the clinical use of the classifier with Bayes' monograph which gives the posterior odds and their 95% CI of positive (nonadherence) versus negative (adherence) cases as predicted by the best-performing classifier. The odds ratio of the posterior probability of positive cases was 3.9, which means that around 10 in every 13 psychiatric patients with a positive result as predicted by our model were following their medication regime. The odds ratio of the posterior probability of true negative cases was 0.4, meaning that around 10 in every 14 psychiatric patients with a negative test result after screening by our classifier were not adhering to their medications. Conclusion Psychiatric medication nonadherence is a large and increasing burden on national health systems. Using Bayesian machine learning techniques and publicly accessible online health forum data, our study illustrates the viability of developing cost-effective, informative decision aids to support the monitoring and prediction of patients at risk of medication nonadherence.

Who killed food tourism? Unaware cannibalism in online conversations about traveling in Italy

PurposeA food tourism destination can fully exploit its competitiveness if food-related attributes are consistently highlighted both in its promotion and in user-generated content. However, in the context of food tourism research, a possible image incongruence has not yet been studied. Tourism destination image incongruence occurs when different travel information sources reflect inconsistent representations of a destination's attributes. This study addresses this gap, focusing on Italian food and wine as drivers to attract visitors. This study examines whether food-related attributes are present in online travel-related conversations and are perceived differently by people with and without knowledge about the destination.Design/methodology/approachContent analysis based on a Bayesian machine-learning technique utilizing Leximancer software was applied to analyze questions and answers posted on TripAdvisor forums by potential and past visitors of four destinations in Italy (Naples, Florence, Parma and Ferrara). Questions and answers expressed by people with different knowledge in Italian and English were analyzed separately to gain deeper understanding.FindingsContrary to expectations, food-related themes were almost completely absent in the conversations analyzed, with only a few exceptions in Italian question sections. This situation depicts a sort of “cannibalism”, in the sense that the centrality of food-related attributes is engulfed by other, less sensorial, enjoyable and memorable aspects of the travel experience.Research limitations/implicationsAnalysis suggests that hype may exist in food tourism promotion related to destination image incongruence. However, while based on a large volume of conversations, the analysis covers only four Italian cities.Practical implicationsDestination management organizations (DMOs) should develop their strategy and communication considering internal and external elements: their marketing targets on one side and the local culture and attractions' perceptions on the other. Standard marketing processes (segmenting, targeting, positioning) and theories should be put in place. The application of standard marketing dynamics and studies should push the DMOs to understand that the internally perceived cultural values of the touristic destinations could not be known or joint univocally by the global external customers and that a local promotional activity should start with branding and not commercial activities.Originality/valueThis is the first study to suggest the existence of hype in food tourism promotion of Italian destinations and to provide evidence supporting this argument.

A Bayesian machine learning approach to rapidly quantifying the fatigue probability of failure for steel catenary risers

Quantifying the risk of system failure is a key input to a risk-based decision-making process for the in-service integrity management of aging steel catenary risers (SCRs), which are prone to fatigue failure within the touchdown zone (TDZ). However, in practice the computational cost of assessing the probability of failure (POF) for SCRs is prohibitive. In this paper, we propose an efficient framework for quantifying the fatigue POF within the SCR TDZ by using the Bayesian machine learning technique adopting Gaussian Processes (GP) for regression. GP-based predictive models perform stochastic response prediction rapidly and are therefore attractive for rapid evaluation of the SCR fatigue POF. In this paper, we introduce innovative techniques for predicting fatigue damage profiles within the SCR TDZ generated by an irregular sea-state applied to the host vessel at its mean offset position, and for dimensionality reduction in the regression analysis. We utilize a realistic case study to demonstrate the framework, which consists of a representative 20″ SCR connected to a semi-submersible host vessel located in 950 m water depth. In addition, we use a site-specific 32-year hindcast wave data and 1-year measured current data, with associated statistical distributions, for the long-term fatigue loading conditions. We also employ numerical simulation and a random sampling technique to create simulation-based input and output datasets for training and testing of the derived probabilistic simulation-based surrogate. The results show that the proposed method is able to predict the fatigue life of the representative riser efficiently and accurately. Finally, we demonstrate the usefulness of the proposed method by rapidly generating the fatigue POF for the 20″ SCR considering the uncertainties associated with input loads, material properties, geometric parameters, and soil stiffness. • A practical machine-learning frameworks is proposed to enable adopting quantified risk-based process for in-service integrity management of SCRs. • A systematic approach is introduced for developing and verifying probabilistic simulation-based surrogates using the Gaussian Process concept for rapidly predicting the damage profile at SCR touchdown zone (TDZ) due to irregular sea-states. • An efficient methodology is proposed for rapid and accurate quantification of the fatigue probability of failure (POF) at SCRs TDZ. • The accuracy and usefulness of the framework is demonstrated using a realistic case study where the probability distribution of the fatigue life of a representative 20″ SCR is quantified via Monte Carlo Simulation.

Shape-invariant encoding of dynamic primate facial expressions in human perception.

Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.

(Invited) Using Machine Learning to Decode Output of a Mixed-Potential Sensor Array for Automotive Exhaust Monitoring

Introduction In order to meet the latest requirements for more fuel-efficient cars with low emissions, it is essential to equip vehicles with advanced sensor systems whose readouts can be used to determine concentrations of key automotive exhaust pollutants such as NOx, NH3 and C3H8 in real time [1]. Arrays of mixed-potential electrochemical sensors (MPES) provide a competitive sensor platform for this task. MPES are electrochemical devices that develop a non-Nernstian potential due to differences in the redox kinetics of various gas species at each electrode/electrolyte gas interface [2,3]. To mitigate signal drift and poor reproducibility, we have developed a patented MPES design utilizing dense electrodes with an overcoat of porous electrolytes such as yttria-stabilized zirconia (YSZ). This design has resulted in highly stable, reproducible, and durable devices which were tested for up to 1000h of operation [4]. In this study, we have employed an array of four mixed-potential sensors (Cr450, Au475, H545, and Cr470) to monitor gas mixtures that mimic automotive exhaust in a laboratory setting. However, even with the latest sensor designs we observe significant cross-specificity and non-linearity in sensor responses to each target gas. Thus, reliable inference of the concentration (or even the presence) of each constituent gas in a complex mixture requires application of advanced machine-learning techniques that will be the focus of this presentation. The ability to decipher the content of gas or liquid mixtures both quickly and reliably has potential applications in many areas of science and technology, including monitoring of various technological processes and continuous observation of air quality in the interests of ecological studies and national security. Our computational methodology can be readily adapted to various sensor array platforms in which gas or liquid mixtures elicit complex response patterns. Method Our previous work has focused on developing computational models which were based on the fundamental electrochemistry of MPES and utilized detailed quantitative descriptions of gas-sensor interactions [5]. We were able to treat both two- and three-gas mixtures (Fig. 1) and take into account the non-linearities of sensor responses. Our model was used to estimate relative concentrations of C3H8, NH3 and NO2 with respect to NO with the maximum error of 14.0% and the average error of 1.8%. Furthermore, we predicted the absolute concentration of each gas in two- and three-gas mixtures with the average error of 3.2%. In this presentation, we will discuss a novel approach to analyzing sensor array output based on using support vector machines (SVM) to classify gas mixtures into types, thus inferring the number and the identity of gases present in a given test mixture. SVM classification is followed by Gaussian kernel regression, a Bayesian machine-learning technique which allows us to predict the concentrations of each constituent gas. The primary advantages of this two-stage algorithm are its versatility: the approach is independent of the sensor platform employed to provide the measurements but requires retraining. Furthermore, given a novel set of measurements, the algorithm is able to estimate both the expected concentration of each component in the mixture and the corresponding uncertainty of the predictions. Unlike maximum-likelihood methods, Gaussian kernel regression is less prone to overfitting and readily lends itself to model comparisons, allowing us to choose the optimal model complexity given the available data. Discussion and Conclusions There is a growing interest in computational approaches designed to infer gas concentrations in complex mixtures such as diesel exhaust solely on the basis of real-time sensor array readouts. In this presentation, we will discuss SVM/Gaussian kernel regression analysis of two-, three- and four-gas mixtures (Fig. 1) using voltage readouts of an array of four mixed-potential sensors as input. Our machine learning approach is scalable, robust, and easily transferrable to other sensor platforms that are currently employed in numerous scientific and industrial settings. After training the model parameters on gas-mixture data generated in the lab under controlled conditions, the computational model can be employed to predict gas concentrations from novel measurements in real time, as required by real-world applications. Figure 1. Plot of the voltage response (in volts) of three sensors in the array: Cr450, Au475 and H545 (Cr470 was excluded due to its similarity with Cr450). Color coding indicates mixture type.

Learning gene regulatory networks using gaussian process emulator and graphical LASSO.

Large amounts of research efforts have been focused on learning gene regulatory networks (GRNs) based on gene expression data to understand the functional basis of a living organism. Under the assumption that the joint distribution of the gene expressions of interest is a multivariate normal distribution, such networks can be constructed by assessing the nonzero elements of the inverse covariance matrix, the so-called precision matrix or concentration matrix. This may not reflect the true connectivity between genes by considering just pairwise linear correlations. To relax this limitative constraint, we employ Gaussian process (GP) model which is well known as computationally efficient non-parametric Bayesian machine learning technique. GPs are among a class of methods known as kernel machines which can be used to approximate complex problems by tuning their hyperparameters. In fact, GP creates the ability to use the capacity and potential of different kernels in constructing precision matrix and GRNs. In this paper, in the first step, we choose the GP with appropriate kernel to learn the considered GRNs from the observed genetic data, and then we estimate kernel hyperparameters using rule-of-thumb technique. Using these hyperparameters, we can also control the degree of sparseness in the precision matrix. Then we obtain kernel-based precision matrix similar to GLASSO to construct kernel-based GRN. The findings of our research are used to construct GRNs with high performance, for different species of Drosophila fly rather than simply using the assumption of multivariate normal distribution, and the GPs, despite the use of the kernels capacity, have a much better performance than the multivariate Gaussian distribution assumption.

Late Time Attractors of Some Varying Chaplygin Gas Cosmological Models

The goal of this paper is to study new cosmological models where the dark energy is a varying Chaplygin gas. This specific dark energy model with non-linear EoS had been often discussed in modern cosmology. Contrary to previous studies, we consider new forms of non-linear non-gravitational interaction between dark matter and assumed dark energy models. We applied the phase space analysis allowing understanding the late time behavior of the models. It allows demonstrating that considered non-gravitational interactions can solve the cosmological coincidence problem. On the other hand, we applied Bayesian Machine Learning technique to learn the constraints on the free parameters. In this way, we gained a better understanding of the models providing a hint which of them can be ruled out. Moreover, the learning based on the simulated expansion rate data shows that the models cannot solve the H0 tension problem.

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Abstract Experiments are performed on a turbulent swirling flame placed inside a vertical tube whose fundamental acoustic mode becomes unstable at higher powers and equivalence ratios. The power, equivalence ratio, fuel composition, and boundary condition of this tube are varied and, at each operating point, the combustion noise is recorded. In addition, short acoustic pulses at the fundamental frequency are supplied to the tube with a loudspeaker and the decay rates of subsequent acoustic oscillations are measured. This quantifies the linear stability of the system at every operating point. Using this data for training, we show that it is possible for a Bayesian ensemble of neural networks to predict the decay rate from a 300 ms sample of the (unpulsed) combustion noise and therefore forecast impending thermoacoustic instabilities. We also show that it is possible to recover the equivalence ratio and power of the flame from these noise snippets, confirming our hypothesis that combustion noise indeed provides a fingerprint of the combustor's internal state. Furthermore, the Bayesian nature of our algorithm enables principled estimates of uncertainty in our predictions, a reassuring feature that prevents it from making overconfident extrapolations. We use the techniques of permutation importance and integrated gradients to understand which features in the combustion noise spectra are crucial for accurate predictions and how they might influence the prediction. This study serves as a first step toward establishing interpretable and Bayesian machine learning techniques as tools to discover informative relationships in combustor data and thereby build trustworthy, robust, and reliable combustion diagnostics.

Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning

Closed-loop optimization of epitaxial titanium nitride (TiN) thin-film growth was accomplished using metal-organic molecular beam epitaxy (MO-MBE) combined with a Bayesian machine-learning technique and reduced the required number of thin-film growth experiments. Epitaxial TiN thin films grown under the process conditions optimized by the Bayesian approach exhibited abrupt metal–superconductor transitions above 5 K, demonstrating a new approach to the efficient development of less-studied materials, such as transition metal nitrides. The combination of the thin-film growth technique and Bayesian approach is expected to pave the way toward accelerating the development of the automated operation of thin-film growth apparatuses.

Machine learning for achieving Bose-Einstein condensation of thulium atoms

Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which are related to quantum simulations. Various problems may benefit from different atomic species, but cooling down novel species interesting for quantum simulations to BEC temperatures requires a substantial amount of optimization and is usually considered as a hard experimental task. In this work, we implemented the Bayesian machine learning technique to optimize the evaporative cooling of thulium atoms and achieved BEC in an optical dipole trap operating near 532 nm. The developed approach could be used to cool down other novel atomic species to quantum degeneracy without additional studies of their properties.

Multimorbidity and Socioeconomic Deprivation among Older Adults: A Cross-sectional Analysis in Five Canadian Cities Using the CLSA

Individual socioeconomic status is known to correlate with multimorbidity, but the role of neighbourhood-level socioeconomic deprivation is uncertain. Participants from five cities (n = 16,313) from the Canadian Longitudinal Study on Aging were mapped by postal code and their local area socioeconomic deprivation scores were calculated using the Vancouver Area Neighbourhood Deprivation Index. Logistic regression models were used to estimate deprivation-associated odds of multimorbidity, controlling for age, sex, obesity, alcohol consumption, and tobacco use, and validated using a Bayesian machine learning technique. Significant associations with neighbourhood deprivation were observed, underscoring the importance of a person’s local socioeconomic environment in multimorbidity risk, net of key covariates. The implications of the results are discussed in terms of community and health care organization and intervention programs targeting multimorbidity.

The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn's Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?

(1) Background: The high heterogeneity of inflammatory bowel disease (IBD) makes the study of this condition challenging. In subjects affected by Crohn’s disease (CD), extra-intestinal manifestations (EIMs) have a remarkable potential impact on health status. Increasing numbers of patient characteristics and the small size of analyzed samples make EIMs prediction very difficult. Under such constraints, Bayesian machine learning techniques (BMLTs) have been proposed as a robust alternative to classical models for outcome prediction. This study aims to determine whether BMLT could improve EIM prediction and statistical support for the decision-making process of clinicians. (2) Methods: Three of the most popular BMLTs were employed in this study: Naϊve Bayes (NB), Bayesian Network (BN) and Bayesian Additive Regression Trees (BART). They were applied to a retrospective observational Italian study of IBD genetics. (3) Results: The performance of the model is strongly affected by the features of the dataset, and BMLTs poorly classify EIM appearance. (4) Conclusions: This study shows that BMLTs perform worse than expected in classifying the presence of EIMs compared to classical statistical tools in a context where mixed genetic and clinical data are available but relevant data are also missing, as often occurs in clinical practice.

Modeling Analysts’ Recommendations via Bayesian Machine Learning

Individual analysts typically publish recommendations several times per year on the handful of stocks they follow within their specialized fields. How should investors interpret this information? How can they factor in the past performance of individual analysts when assessing whether to invest long or short in a stock? This is a complicated problem to model quantitatively: There are thousands of individual analysts, each of whom follows only a small subset of the thousands of stocks available for investment. Overcoming this inherent sparsity naturally raises the question of how to learn an analyst’s forecasting ability by integrating track-record information from all the stocks the analyst follows; in other words, inferring an analyst’s ability on Stock X from track records on both Stock X and stocks other than X. The authors address this topic using a state-of-the-art computationally rapid Bayesian machine learning technique called independent Bayesian classifier combination (IBCC), which has been deployed in the physical and biological sciences. The authors argue that there are many similarities between the analyst forecasting problem and a very successful application of IBCC in astronomy, a study in which it dominates heuristic alternatives including simple or weighted averages and majority voting. The IBCC technique is ideally suited to this particularly sparse problem, enabling computationally efficient inference, dynamic tracking of analyst performance through time, and real-time online forecasting. The results suggest the IBCC technique holds promise in extracting information that can be deployed in active discretionary and quantitative investment management.

Bayesian approach to model-based extrapolation of nuclear observables

The mass, or binding energy, is the basis property of the atomic nucleus. It determines its stability, and reaction and decay rates. Quantifying the nuclear binding is important for understanding the origin of elements in the universe. The astrophysical processes responsible for the nucleosynthesis in stars often take place far from the valley of stability, where experimental masses are not known. In such cases, missing nuclear information must be provided by theoretical predictions using extreme extrapolations. Bayesian machine learning techniques can be applied to improve predictions by taking full advantage of the information contained in the deviations between experimental and calculated masses. We consider 10 global models based on nuclear Density Functional Theory as well as two more phenomenological mass models. The emulators of S2n residuals and credibility intervals defining theoretical error bars are constructed using Bayesian Gaussian processes and Bayesian neural networks. We consider a large training dataset pertaining to nuclei whose masses were measured before 2003. For the testing datasets, we considered those exotic nuclei whose masses have been determined after 2003. We then carried out extrapolations towards the 2n dripline. While both Gaussian processes and Bayesian neural networks reduce the rms deviation from experiment significantly, GP offers a better and much more stable performance. The increase in the predictive power is quite astonishing: the resulting rms deviations from experiment on the testing dataset are similar to those of more phenomenological models. The empirical coverage probability curves we obtain match very well the reference values which is highly desirable to ensure honesty of uncertainty quantification, and the estimated credibility intervals on predictions make it possible to evaluate predictive power of individual models.

Predictive models of minimal hepatic encephalopathy for cirrhotic patients based on large-scale brain intrinsic connectivity networks

We aimed to find the most representative connectivity patterns for minimal hepatic encephalopathy (MHE) using large-scale intrinsic connectivity networks (ICNs) and machine learning methods. Resting-state fMRI was administered to 33 cirrhotic patients with MHE and 43 cirrhotic patients without MHE (NMHE). The connectivity maps of 20 ICNs for each participant were obtained by dual regression. A Bayesian machine learning technique, called Graphical Model-based Multivariate Analysis, was applied to determine ICN regions that characterized group differences. The most representative ICNs were evaluated by the performance of three machine learning methods (support vector machines (SVMs), multilayer perceptrons (MLP), and C4.5). The clinical significance of these potential biomarkers was further tested. The temporal lobe network (TLN), and subcortical network (SCN), and sensorimotor network (SMN) were selected as representative ICNs. The distinct functional integration patterns of the representative ICNs were significantly correlated with behavior criteria and Child-Pugh scores. Our findings suggest the representative ICNs based on GAMMA can distinguish MHE from NMHE and provide supplementary information to current MHE diagnostic criteria.

The Consequences of Contact with the Criminal Justice System for Health in the Transition to Adulthood.

A rapidly growing literature has documented the adverse social, economic and, recently, health impacts of experiencing incarceration in the United States. Despite the insights that this work has provided in consistently documenting the deleterious effects of incarceration, little is known about the specific timing of criminal justice contact and early health consequences during the transition from adolescence to adulthood-a critical period in the life course, particularly for the development of poor health. Previous literature on the role of incarceration has also been hampered by the difficulties of parsing out the influence that incarceration exerts on health from the social and economic confounding forces that are linked to both criminal justice contact and health. This paper addresses these two gaps in the literature by examining the association between incarceration and health in the United States during the transition to adulthood, and by using an analytic approach that better isolates the association of incarceration with health from the multitude of confounders which could be alternatively driving this association. In this endeavor, we make use of variable-rich data from The National Longitudinal Study of Adolescent to Adult Health (n = 10,785) and a non-parametric Bayesian machine learning technique- Bayesian Additive Regression Trees. Our results suggest that the experience of incarceration at this stage of the life course increases the probability of depression, adversely affects the perception of general health status, but has no effect on the probability of developing hypertension in early adulthood. These findings signal that incarceration in emerging adulthood is an important stressor that can have immediate implications for mental and general health in early adulthood, and may help to explain long lasting implications incarceration has for health across the life course.