P-spline smoothing for spatial data collected worldwide

Bayesian multiscale analysis of images modeled as Gaussian Markov random fields

In this paper we derive score tests for spatial independence in mortality or incidence risk in the framework of hierarchical spatial models where different Gaussian Markov random field (MRF) priors are given for modelling the area random effects (specifically, two non-intrinsic Gaussian priors and a convolution Gaussian prior). The techniques used to test the practically relevant and important simplifying hypotheses of an absence of spatial variation in risk will provide a guidance for practitioners to select an adequate model (i.e., a model with an exchangeable-independent-prior, an intrinsic prior, a convolution prior or a non-intrinsic prior, for the area-specific random effects distribution). The proposed methodology is illustrated by analyzing the well-known data set of lip cancer in Scotland and female mortality due to cerebrovascular disease in Navarra, Spain.

In ionospheric tomography, the atmospheric electron density is reconstructed from different electron density related measurements, most often from ground-based measurements of satellite signals. Typically, ionospheric tomography suffers from two major complications. First, the information provided by measurements is insufficient and additional information is required to obtain a unique solution. Second, with necessary spatial and temporal resolutions, the problem becomes very high dimensional, and hence, computationally infeasible. With Bayesian framework, the required additional information can be given with prior probability distributions. The approach then provides physically quantifiable probabilistic interpretation for all model variables. Here, Gaussian Markov random fields (GMRFs) are used for constructing the prior electron density distribution. The use of GMRF introduces sparsity to the linear system, making the problem computationally feasible. The method is demonstrated over Fennoscandia with measurements from global navigation satellite system (GNSS) and low Earth orbit (LEO) satellite receiver networks, GNSS occultation receivers, LEO satellite Langmuir probes, and ionosonde and incoherent scatter radar measurements.

We apply a full Bayesian model framework to a dataset on stomach cancer mortality in West Germany. The data are stratified by age group, year, and district. Using an age-period-cohort model with an additional spatial component, our goal is to investigate whether there is evidence for space-time interactions in these data. Furthermore, we will determine whether a period-space or a cohort-space interaction model is more appropriate to predict future mortality rates. The setup will be fully Bayesian based on a series of Gaussian Markov random field priors for each of the components. Statistical inference is based on efficient algorithms to block update Gaussian Markov random fields, which have recently been proposed in the literature.

The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present adaPop, a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2.

We used the 2013 Nigeria Demographic Health Survey (NDHS) data to investigate the determinants of family size in Nigeria using the geo-additive model. The model was used to simultaneously measure the fixed, nonlinear, spatial and random effects. The fixed effect of categorical covariates were modeled using the diffuse prior, P-spline with secondorder random walk for the nonlinear effect of continuous variable, spatial effects followed Markov random field priors while the exchangeable normal priors were used for the random effects of the community and household. The negative binomial distribution was used to handle over dispersion of the dependent variable. Inference was fully Bayesian approach. Results showed a declining effect of secondary and higher education of mother, Yoruba tribe, Christianity, family planning, mother giving birth by caesarean section and having a partner who has secondary education on family size. Family size is positively associated with age at first birth, number of daughters in a household, being gainfully employed, married and living with partner, community and household effects.

Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size , a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on , the coalescent and the sampling processes can be jointly modeled to improve estimation of . Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide efficient algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref. Supplementary files for this article are available online.

Accurate forecasting of diabetes burden is essential for healthcare planning, resource allocation, and policy-making. While deep learning models have demonstrated superior predictive capabilities, their real-world applicability is constrained by computational complexity and data quality challenges. This study evaluates the trade-offs between predictive accuracy, robustness, and computational efficiency in diabetes forecasting. Four forecasting models were selected based on their ability to capture temporal dependencies and handle missing healthcare data: Transformer with Variational Autoencoder (VAE), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and AutoRegressive Integrated Moving Average (ARIMA). Annual data on Disability-Adjusted Life Years (DALYs), Deaths, and Prevalence from 1990 to 2021 were used to train (1990–2014) and evaluate (2015–2021) the models. Performance was measured using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Robustness tests introduced noise and missing data, while computational efficiency was assessed in terms of training time, inference speed, and memory usage. Statistical significance was analyzed using ANOVA and Tukey’s post-hoc tests. The Transformer-VAE model achieved the highest predictive accuracy (MAE: 0.425, RMSE: 0.501) and demonstrated superior resilience to noisy and incomplete data (p < 0.01). LSTM effectively captured short-term patterns but struggled with long-term dependencies, while GRU, though computationally efficient, exhibited higher error rates. ARIMA, despite being resource-efficient, showed limited capability in modeling long-term trends, indicating potential benefits in hybrid approaches. While Transformer-VAE provides the most accurate diabetes burden forecasting, its high computational cost and interpretability challenges limit its scalability in resource-constrained settings. These findings highlight the potential of deep learning models for healthcare forecasting, while underscoring the need for further validation before integration into real-world public health decision-making.

Whole genome prediction (WGP) modeling and genome-wide association (GWA) analyses are big data issues in agricultural quantitative genetics. Both areas require meaningful input from the statistical scholarly community in order to further improve the accuracy of prediction of genetic merit and inference on putative causal variants as well as improving the computational efficiency of existing methods and algorithms. These concerns have become increasingly critical as new sequencing technologies will only exacerbate current model dimensionality problems. We focus primarily on mixed model and hierarchical Bayesian analyses which have been most commonly pursued by animal and plant breeders for WGP thus far. We draw attention to our observation that many such previous analyses have not carefully inferred upon hyperparameters defined at the top levels of the Bayesian model hierarchy, but simply arbitrarily specify their values. We also reassess previous discussions on WGP model dimensionality, believing that useful data augmentation schemes utilized in various Markov Chain Monte Carlo (MCMC) schemes have led to a general misunderstanding that heavy-tailed or variable selection-based WGP models may be highly parameterized relative to more standard mixed model representations. Computational efficiency is addressed with respect to MCMC and competitive, albeit approximate, alternatives. Furthermore, GWA analyses are reassessed, encouraging a greater reliance on shrinkage-based inferences based on critically chosen priors, instead of potentially nonreproducible fixed effects P value-based inference.

Model-in-the-Loop (MIL) simulation is a testing technique widely used in the automotive industry for developing and testing control systems. It involves integrating a mathematical model of a physical system being developed into a virtual environment and performing rigorous testing. MIL simulation offers benefits in system design and development, but it also has several computational challenges that can impact the efficiency of the simulation. This paper focuses on enhancing the computational efficiency of a MIL simulation environment for Electric Vehicle (EV) Powertrain system using a Reduced Order Model (ROM) with Artificial Intelligence (AI). The EV MIL simulation environment has been developed using a first-principles model in MATLAB/Simulink to analyze the system performance. However, system level MIL simulation performance is poor due to high-fidelity plant models. This paper's objective is to accelerate system level MIL simulation performance by leveraging ROM using AI techniques. To achieve this, the high computational high-fidelity plant model has been replaced with trained Neural State Space (NSS) ROM model using MATLAB/Simulink tool. Simulation results show that the AI driven ROM model achieved an accuracy of 85% compared to the estimation data. This implementation has improved the overall MIL simulation computational performance by three times. This approach enables a simulation engineer to analyze or simulate complex systems by significantly reducing computation time while preserving the expected fidelity within a controlled error margin. Keywords: Model-in-the-Loop, simulation, testing control system, MIL, system design, computational efficiency, EV, powertrain system, Electric Vehicle, artificial intelligence (AI), MATLAB, Simulink

The computational challenge in solving dynamic models of power distribution grids increases with the high penetration of distributed photovoltaic (PV) systems. The existing dynamic models of PV systems are overly detailed and computationally intractable for solving distribution grid dynamics with a large number of distributed PV systems. As IEEE-1547 requires smart PV inverters to support the grid voltage and frequency dynamically, this motivates to develop an accurate and computationally efficient dynamic model of distributed PVs. Therefore, in this work, a dynamic phasor model of smart PV inverters is developed and compared with an existing electromagnetic transient model (detailed model) in terms of accuracy and computational efficiency. The results show a remarkably fast solve time of the proposed dynamic phasor model compared to that of the detailed model (more than 30 times speed up), while sufficiently capturing necessary volt-var, volt-watt, and frequency-watt dynamics during the normal, and voltage ride-through and frequency ride-through events.

ABSTRACTContour maps are widely used to display estimates of spatial fields. Instead of showing the estimated field, a contour map only shows a fixed number of contour lines for different levels. However, despite the ubiquitous use of these maps, the uncertainty associated with them has been given a surprisingly small amount of attention. We derive measures of the statistical uncertainty, or quality, of contour maps, and use these to decide an appropriate number of contour lines, which relates to the uncertainty in the estimated spatial field. For practical use in geostatistics and medical imaging, computational methods are constructed, that can be applied to Gaussian Markov random fields, and in particular be used in combination with integrated nested Laplace approximations for latent Gaussian models. The methods are demonstrated on simulated data and an application to temperature estimation is presented.

Cloud computing is highly integrated with modern digital infrastructures, expansion of computing, and its services being flexible and adaptable. However, with the increase of cyber threats, the challenge of quantum computing and cyber threats remains unprecedented. The potential of quantum computing remains a complete challenge to establish classical encryption techniques. The difficulty with traditional security techniques is the equilibrium between adaptive shielding and effective computing. The lack of shielding in cloud systems is a complete lack of computational efficiency and bear breaches of cloud systems. This computational efficiency is the gap which this research intends to bridge with Quantum-Resilient Security Optimization for Cloud Architectures (Q-SECURE-OPT). The novel integration with parallel adaptation cloud security, to be discussed in this document, involves the integration of optimization frameworks with post-quantum cryptographic methods. Through the use of parallel processing, adaptive heuristics make latency of real-time intrusion detection, security resource allocation, active optimization of key distribution, and intrusion deqetection adaptable to a virtually 0 gradient of a target function parallel closure. This research demonstrated that post-quantum cryptographic methods enhance encryption efficiency to the extent of 35% and analytically withstand quantum attacks directed through adaptive heuristics. The results indicate that Q-SECURE-OPT not only strengthens the security posture of cloud infrastructures but also ensures scalability, responsiveness, and robust adaptive performance under diverse workload conditions.

This article introduces novel and practicable Bayesian factor analysis frameworks that are computationally feasible for moderate to large spatiotemporal data. Previous Bayesian analysis of spatiotemporal data has used a Bayesian factor model with separable temporal latent factors and spatial factor loadings, along with stick-breaking process priors on the loadings to enable clustering of spatial locations. Such a flexible Bayesian model, however, faces a prohibitively high computational cost in posterior sampling when the spatial and temporal dimensions increase to a couple hundred. We address this computational challenge with several speed-up proposals. We integrate a new slice sampling algorithm that permits varying numbers of spatial mixture components across all latent factors and guarantees them to be nonincreasing through the posterior sampling iterations, thus, effectively reducing the number of mixture parameters. Additionally, we introduce a spatial latent nearest-neighbor Gaussian process prior and new sequential updating algorithms for the spatially varying latent variables in the stick-breaking process prior. Our new models and sampling algorithms exhibit significantly enhanced computational scalability and storage efficiency and possess powerful inferential capabilities for both spatiotemporal prediction and clustering of spatial locations with similar temporal trajectories. The improvement in computational efficiency and inferential performance is substantiated by extensive simulation experiments.

The burgeoning field of genomics has revived interest in multiple testing procedures by raising new methodological and computational challenges. For example, microarray experiments generate large multiplicity problems in which thousands of hypotheses are tested simultaneously. Westfall and Young (1993) propose resampling-based p-value adjustment procedures which are highly relevant to microarray experiments. This article discusses dierent criteria for error control in resampling-based multiple testing, including (a) the family wise error rate of Westfall and Young (1993) and (b) the false discovery rate developed by Benjamini and Hochberg (1995), both from a frequentist viewpoint; and (c) the positive false discovery rate of Storey (2002a), which has a Bayesian motivation. We also introduce our recently developed fast algorithm for implementing the minP adjustment to control family-wise error rate. Adjusted p-values for dierent approaches are applied to gene expression data from two recently published microarray studies. The properties of these procedures for multiple testing are compared.