Bayesian model updating (BMU) is frequently used in Structural Health Monitoring to investigate the structure’s dynamic behavior under various operational and environmental loadings for decision-making, e.g., to determine whether maintenance is required. Data collected by sensors are used to update the prior of some physics-based model’s latent parameters to yield the posterior. The choice of prior may significantly affect posterior predictions and subsequent decision-making, especially under the typical case in engineering applications of little informative data. Therefore, understanding how the choice of prior affects the posterior prediction is of great interest. In this article, a robust Bayesian inference technique evaluates the optimal and worst-case prior in the vicinity of a chosen nominal prior and their corresponding posteriors. This technique derives an interacting Wasserstein gradient flow that minimizes and maximizes/minimizes the KL divergence between the posterior and the approximation to the posterior, with respect to the approximation to the posterior and the prior. Two numerical case studies are used to showcase the proposed algorithm: a double-banana-posterior and a double-beam structure. Optimal and worst-case priors are modeled by specifying an ambiguity set containing any distribution at a statistical distance to the nominal prior, less than or equal to the radius. The resulting posteriors may be used to yield the lower and upper bounds on subsequent calculations of an engineering metric (e.g., failure probability) used for decision-making. If the metric used for decision-making is not sensitive to the resulting posteriors, it may be assumed that decisions taken are robust to prior uncertainty.

Robotic manipulation inherently involves contact with objects for task accomplishment. Traditional motion planning techniques, while having shown their success in collision-free scenarios, may not handle manipulation tasks effectively because they typically avoid contact. Although geometric constraints have been introduced into classical motion planners for tasks that involve interactions, they still lack the capability to fully incorporate contact. In addition, these planning methods generally do not operate on objects that cannot be directly controlled. In this work, building on a recently proposed framework for energy-based quasi-static manipulation, we propose an approach to manipulation planning by adapting a numerical continuation algorithm to compute the equilibrium manifold (EM), which is implicitly derived from physical laws. By defining a manipulation potential energy function that captures interaction and natural potentials, the numerical continuation approach is integrated with adaptive ordinary differential equations that converge to the EM. This allows discretizing the implicit manifold as a graph with a finite set of equilibria as nodes interconnected by weighted edges defined via a haptic metric. The proposed framework is evaluated with an inverted pendulum task, where the explored branch of the manifold demonstrates effectiveness.

We propose a family of weighted statistics based on the CUSUM process of the WLS residuals for the online detection of changepoints in a Random Coefficient Autoregressive model, using both the standard CUSUM and the Page-CUSUM process. We derive the asymptotics under the null of no changepoint for all possible weighing schemes, including the case of the standardized CUSUM, for which we derive a Darling–Erdös-type limit theorem; our results guarantee the procedure-wise size control under both an open-ended and a closed-ended monitoring. In addition to considering the standard RCA model with no covariates, we also extend our results to the case of exogenous regressors. Our results can be applied irrespective of (and with no prior knowledge required as to) whether the observations are stationary or not, and irrespective of whether they change into a stationary or nonstationary regime. Hence, our methodology is particularly suited to detect the onset, or the collapse, of a bubble or an epidemic. Our simulations show that our procedures, especially when standardising the CUSUM process, can ensure very good size control and short detection delays. We complement our theory by studying the online detection of breaks in epidemiological and housing prices series.

For a continuous-time phase-type (PH) distribution, starting with its Laplace–Stieltjes transform, we obtain a necessary and sufficient condition for its minimal PH representation to have the same order as its algebraic degree. To facilitate finding this minimal representation, we transform this condition equivalently into a non-convex optimization problem, which can be effectively addressed using an alternating minimization algorithm. The algorithm convergence is also proved. Moreover, the method we develop for the continuous-time PH distributions can be used directly for the discrete-time PH distributions after establishing an equivalence between the minimal representation problems for continuous-time and discrete-time PH distributions.

In the present technological age, where cyber-risk ranks alongside natural and man-made disasters and catastrophes – in terms of global economic loss – businesses and insurers alike are grappling with fundamental risk management issues concerning the quantification of cyber-risk, and the dilemma as to how best to mitigate this risk. To this end, the present research deals with data, analysis, and models with the aim of quantifying and understanding cyber-risk – often described as “holy grail” territory in the realm of cyber-insurance and IT security. Nonparametric severity models associated with cyber-related loss data – identified from several competing sources – and accompanying parametric large-loss components, are determined, and examined. Ultimately, in the context of analogous cyber-coverage, cyber-risk is quantified through various types and levels of risk adjustment for (pure-risk) increased limit factors, based on applications of actuarially founded aggregate loss models in the presence of various forms of correlation. By doing so, insight is gained into the nature and distribution of volatile severity risk, correlated aggregate loss, and associated pure-risk limit factors.

Bartonella is a widely distributed Gram-negative bacterium that includes species that are capable of causing illness in humans. Rodents represent one of the main reservoirs of zoonotic pathogens, and monitoring their populations can provide valuable insights into human health. We conducted a surveillance study of rodents from two north-western states of Mexico (Baja California and Chihuahua) to investigate the prevalence and genetic diversity of Bartonella by polymerase chain reaction (PCR) amplification and sequencing of the citrate synthase (gltA) gene. A total of 586 rodents belonging to 28 species were captured, and 408 were tested for Bartonella spp. The overall Bartonella spp. prevalence was 39.71%. The prevalence found in Chihuahua was higher (42.80%) than in Baja California (32.52%), and rodents such as Neotoma albigula, Neotoma mexicana, Peromyscus boylii, and Chaetodipus baileyi had the highest prevalence. The gltA sequences revealed seven genetic variants, some of which were obtained from Peromyscus and Dipodomys rodents and were associated with Bartonella species of human health concern, such as B. grahamii and B. vinsonii subsp. arupensis. In addition, a sequence obtained from a Peromyscus maniculatus was clustered with Candidatus Bartonella rudakovii, a previously unreported association. This study provides valuable data and new insight into the Bartonella-hosts interactions in rodent species in north-western Mexico.

Data for Policy (dataforpolicy.org), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.

Stochastic generators are essential to produce synthetic realizations that preserve target statistical properties. We propose GenFormer, a stochastic generator for spatio-temporal multivariate stochastic processes. It is constructed using a Transformer-based deep learning model that learns a mapping between a Markov state sequence and time series values. The synthetic data generated by the GenFormer model preserve the target marginal distributions and approximately capture other desired statistical properties even in challenging applications involving a large number of spatial locations and a long simulation horizon. The GenFormer model is applied to simulate synthetic wind speed data at various stations in Florida to calculate exceedance probabilities for risk management.

A new explicit solution representation is provided for ARMA recursions with drift and either deterministically or stochastically varying coefficients. It is expressed in terms of the determinants of banded Hessenberg matrices and, as such, is an explicit function of the coefficients. In addition to computational efficiency, the proposed solution provides a more explicit analysis of the fundamental properties of such processes, including their Wold–Cramér decomposition, their covariance structure, and their asymptotic stability and efficiency. Explicit formulae for optimal linear forecasts based either on finite or infinite sequences of past observations are provided. The practical significance of the theoretical results in this work is illustrated with an application to U.S. inflation data. The main finding is that inflation persistence increased after 1976, whereas from 1986 onward, the persistence declines and stabilizes to even lower levels than the pre-1976 period.

Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the nonsingularity of a high-dimensional matrix or operator and may fail for finite samples or even at the population level. Regularized estimators provide a solution by enabling consistent estimation of structural or average structural functions, sometimes even under identification failure. We show that the asymptotic distribution in these cases can be nonstandard. We develop a comprehensive theory of regularized estimators, which include methods such as high-dimensional ridge regularization, gradient descent, and principal component analysis (PCA). The results are illustrated for high-dimensional and nonparametric instrumental variable regressions and are supported through simulation experiments.

We study minimax regret treatment rules under matched treatment assignment in a setup where a policymaker, informed by a sample of size N, needs to decide between T different treatments for a $T\geq 2$. Randomized rules are allowed for. We show that the generalization of the minimax regret rule derived in Schlag (2006, ELEVEN—Tests needed for a recommendation, EUI working paper) and Stoye (2009, Journal of Econometrics 151, 70–81) for the case $T=2$ is minimax regret for general finite $T>2$ and also that the proof structure via the Nash equilibrium and the “coarsening” approaches generalizes as well. We also show by example, that in the case of random assignment the generalization of the minimax rule in Stoye (2009, Journal of Econometrics 151, 70–81) to the case $T>2$ is not necessarily minimax regret and derive minimax regret rules for a few small sample cases, e.g., for $N=2$ when $T=3.$

In the case where a covariate x is included, it is shown that a minimax regret rule is obtained by using minimax regret rules in the “conditional-on-x” problem if the latter are obtained as Nash equilibria.

The cumulative residual extropy has been proposed recently as an alternative measure of extropy to the cumulative distribution function of a random variable. In this paper, the concept of cumulative residual extropy has been extended to cumulative residual extropy inaccuracy (CREI) and dynamic cumulative residual extropy inaccuracy (DCREI). Some lower and upper bounds for these measures are provided. A characterization problem for the DCREI measure under the proportional hazard rate model is studied. Nonparametric estimators for CREI and DCREI measures based on kernel and empirical methods are suggested. Also, a simulation study is presented to evaluate the performance of the suggested measures. Simulation results show that the kernel-based estimator performs better than the empirical-based estimator. Finally, applications of the DCREI measure for model selection are provided using two real data sets.

We propose Rényi information generating function (RIGF) and discuss its properties. A connection between the RIGF and the diversity index is proposed for discrete-type random variables. The relation between the RIGF and Shannon entropy of order q > 0 is established and several bounds are obtained. The RIGF of escort distribution is derived. Furthermore, we introduce the Rényi divergence information generating function (RDIGF) and discuss its effect under monotone transformations. We present nonparametric and parametric estimators of the RIGF. A simulation study is carried out and a real data relating to the failure times of electronic components is analyzed. A comparison study between the nonparametric and parametric estimators is made in terms of the standard deviation, absolute bias, and mean square error. We have observed superior performance for the newly proposed estimators. Some applications of the proposed RIGF and RDIGF are provided. For three coherent systems, we calculate the values of the RIGF and other well-established uncertainty measures, and similar behavior of the RIGF is observed. Further, a study regarding the usefulness of the RDIGF and RIGF as model selection criteria is conducted. Finally, three chaotic maps are considered and then used to establish a validation of the proposed information generating function.

This work studies the reliability function of K-out-of-N systems with a general repair time distribution and a single repair facility. It introduces a new repair mechanism using an effort function, described by a nonlinear ordinary differential equation. Three theoretical results are obtained: regularity properties preventing simultaneous failures and repairs, derivation of a Kolmogorov forward system for micro-state and macro-state probabilities, and comparison of reliability functions of two K-out-of-N systems. An additional hypothesis on the model’s parameters allows us to obtain an ordering relation between the reliability functions. A numerical example demonstrates the model’s practical application and confirms the theoretical results.

The various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, ranging from predictive models to foresight techniques, emerge as valuable tools for policymakers. These methods, now bolstered by advancements in technology and leveraging nontraditional data sources, can offer a pathway to develop more precise, responsive, and forward-thinking policies.

This paper seeks to map out the rapidly evolving domain of anticipatory methods in the realm of migration policy, capturing the trend toward integrating quantitative and qualitative methodologies and harnessing novel tools and data. It introduces a new taxonomy designed to organize these methods into three core categories: Experience-based, Exploration-based, and Expertise-based. This classification aims to guide policymakers in selecting the most suitable methods for specific contexts or questions, thereby enhancing migration policies.

Our study aimed to describe the transmission dynamics and genotypic diversity of Mycobacterium tuberculosis in people deprived of liberty (PDL) in four Colombian prisons. Our cohort study included 64 PDL with bacteriologically confirmed pulmonary tuberculosis diagnosed in four Colombian prisons. The 132 isolates were genotyped using 24-mycobacterial interspersed repeated units-variable number tandem repeats (MIRUs-VNTR). A cluster was defined when ≥2 isolates from different PDL had the same genotype. Tuberculosis acquired in prison was considered when ≥2 persons were within the same cluster and had an epidemiological link. We mapped the place of residence before incarceration and within prisons. We assessed overcrowding and ventilation conditions in the prison that had clusters. We found that the most frequent genotypes were LAM (56.8%) and Haarlem (36.4%), and 45.3% of the PDL diagnosed with tuberculosis were clustered. Most PDL diagnosed in prison came from neighborhoods in Medellin with a high TB incidence. M. tuberculosis infection acquired in prison was detected in 19% of PDL, 9.4% had mixed infection, 3.1% reinfection, and 1.6% relapse. Clusters only appeared in one prison, in cell blocks with overcrowding >100%, and inadequate ventilation conditions. Prisons require the implementation of effective respiratory infection control measures to prevent M. tuberculosis transmission.

In this paper, we investigate the number of customers that overlap or coincide with a virtual customer in an Erlang-A queue. Our analysis starts with the fluid and diffusion limit differential equations to obtain the mean and variance of the queue length. We then develop precise approximations for waiting times using fluid limits and the polygamma function. Building on this, we introduce a novel approximation scheme to calculate the mean and variance of the number of overlapping customers. This method facilitates the assessment of transient overlap risks in complex service systems, offering a useful tool for service providers to mitigate significant overlaps during pandemic seasons.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g., structural health monitoring), feature-label pairs used to learn such mappings are of limited availability, which hinders the effectiveness of traditional supervised machine learning approaches. This paper proposes a methodology for overcoming the issue of data scarcity by combining active learning (AL) for regression with hierarchical Bayesian modeling. AL is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g., inspection and maintenance). Hierarchical Bayesian modeling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modeling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks, which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modeling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost—maintaining predictive performance while reducing the number of inspections required.

This paper proposes a consistent nonparametric test with good sampling properties to detect instantaneous causality between vector autoregressive (VAR) variables with time-varying variances. The new test takes the form of the U-statistic, and has a limiting standard normal distribution under the null. We further show that the test is consistent against any fixed alternatives, and has nontrivial asymptotic power against a class of local alternatives with a rate slower than $T^{-1/2}$. We also propose a wild bootstrap procedure to better approximate the finite sample null distribution of the test statistic. Monte Carlo experiments are conducted to highlight the merits of the proposed test relative to other popular tests in finite samples. Finally, we apply the new test to investigate the instantaneous causality relationship between money supply and inflation rates in the USA.