Bayesian Simulation Research Articles

Is artificial intelligence a hazardous technology? Economic trade-off model

Artificial intelligence (AI) demonstrates various opportunities and risks. Our study explores the trade-off of AI technology, including existential risks. We develop a theory and a Bayesian simulation model in order to explore what is at stake. The study reveals four tangible outcomes: (i) regulating existential risks has a boundary solution of either prohibiting the technology or allowing a laissez-faire regulation. (ii) the degree of ‘normal’ risks follows a trade-off and is dependent on AI-intensity. (iii) we estimate the probability of ‘normal’ risks to be between 0.002% to 0.006% over a century. (iv) regulating AI requires a balanced and international approach due to the dynamic risks and its global nature.

Bayesian spatio-temporal modelling of environmental, climatic, and socio-economic influences on malaria in Central Vietnam

BackgroundDespite the successful efforts in controlling malaria in Vietnam, the disease remains a significant health concern, particularly in Central Vietnam. This study aimed to assess correlations between environmental, climatic, and socio-economic factors in the district with malaria cases.MethodsThe study was conducted in 15 provinces in Central Vietnam from January 2018 to December 2022. Monthly malaria cases were obtained from the Institute of Malariology, Parasitology, and Entomology Quy Nhon, Vietnam. Environmental, climatic, and socio-economic data were retrieved using a Google Earth Engine script. A multivariable Zero-inflated Poisson regression was undertaken using a Bayesian framework with spatial and spatiotemporal random effects with a conditional autoregressive prior structure. The posterior random effects were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling.ResultsThere was a total of 5,985 Plasmodium falciparum and 2,623 Plasmodium vivax cases during the study period. Plasmodium falciparum risk increased by five times (95% credible interval [CrI] 4.37, 6.74) for each 1-unit increase of normalized difference vegetation index (NDVI) without lag and by 8% (95% CrI 7%, 9%) for every 1ºC increase in maximum temperature (TMAX) at a 6-month lag. While a decrease in risk of 1% (95% CrI 0%, 1%) for a 1 mm increase in precipitation with a 6-month lag was observed. A 1-unit increase in NDVI at a 1-month lag was associated with a four-fold increase (95% CrI 2.95, 4.90) in risk of P. vivax. In addition, the risk increased by 6% (95% CrI 5%, 7%) and 3% (95% CrI 1%, 5%) for each 1ºC increase in land surface temperature during daytime with a 6-month lag and TMAX at a 4-month lag, respectively. Spatial analysis showed a higher mean malaria risk of both species in the Central Highlands and southeast parts of Central Vietnam and a lower risk in the northern and north-western areas.ConclusionIdentification of environmental, climatic, and socio-economic risk factors and spatial malaria clusters are crucial for designing adaptive strategies to maximize the impact of limited public health resources toward eliminating malaria in Vietnam.

Are Wearable Photoplethysmogram-Based Heart Rate Variability Measures Equivalent to Electrocardiogram? A Simulation Study.

Traditional electrocardiography (ECG)-derived heart rate variability (HRV) and photoplethysmography (PPG)-derived "HRV" (termed PRV) have been reported interchangeably. Any potential dissociation between HRV and PRV could be due to the variability in pulse arrival time (PAT; time between heartbeat and peripheral pulse). This study examined if PRV is equivalent to ECG-derived HRV and if PRV's innate error makes it a high-quality measurement separate from HRV. ECG data from 1084 subjects were obtained from the PhysioNet Autonomic Aging dataset, and individual PAT dispersions for both the wrist (n = 42) and finger (n = 49) were derived from Mol et al. (Exp Gerontol. 2020; 135: 110938). A Bayesian simulation was constructed whereby the individual arrival times of the PPG wave were calculated by placing a Gaussian prior on the individual QRS-wave timings of each ECG series. The standard deviation (σ) of the prior corresponds to the PAT dispersion from Mol et al. This was simulated 10,000 times for each PAT σ. The root mean square of successive differences (RMSSD) and standard deviation of N-N intervals (SDNN) were calculated for both HRV and PRV. The Region of Practical Equivalence bounds (ROPE) were set a priori at ± 0.2% of true HRV. The highest density interval (HDI) width, encompassing 95% of the posterior distribution, was calculated for each PAT σ. The lowest PAT σ (2.0 SD) corresponded to 88.4% within ROPE for SDNN and 21.4% for RMSSD. As the σ of PAT increases, the equivalence of PRV and HRV decreases for both SDNN and RMSSD. The HDI interval width increases with increasing PAT σ, with the HDI width increasing at a higher rate for RMSSD than SDNN. For individuals with greater PAT variability, PRV is not a surrogate for HRV. When considering PRV as a unique biometric measure, SDNN may have more favorable measurement properties than RMSSD, though both exhibit a non-uniform measurement error.

Decoupling the multi-drivers of urban extreme heat environment in urban agglomerations using ensemble learning

Urban extreme heat (UEH) has been imparting a formidable menace to various facets of sustainable urban and regional development. While increasing studies have examined the urban thermal environment, a gap exists in decoupling the nonlinear multi-drivers of UEH environment in intra-urban regions, especially across the urban agglomerations. In this study, we estimated UEH through the land surface temperature (LST) in the extended summer season exceeding a specific threshold computed through cross-regional statistics. Furthermore, an additive interpretable ensemble learning model enhanced via the Bayesian Optimization algorithm (BO) and Monte Carlo Simulation framework (MCS) was employed to dissect the intricate nonlinear interplay between UEH and intra-urban morphology, green-blue composition, and atmospheric determinants. Our results highlight the indispensably suppressive effect of green and blue factors on UEH, whereas urban morphological variables generally exhibit an opposite trend. Interestingly, we reveal heterogeneity responses of building heights, elevated urban structures mitigate UEH in temperate monsoon and subtropical monsoon zones and exhibit diminished marginal utility in temperate continental zones. Furthermore, the arid climatic characteristics are expected to exert an unexpectedly enhancing effect on water bodies' cooling capacity. These findings provide heterogeneous local guidance for landscape and urban planning towards the severe challenges of climate change.

Process Damping Identification Using Bayesian Learning and Time Domain Simulation

Abstract Process damping can provide improved machining productivity by increasing the stability limit at low spindle speeds. While the phenomenon is well known, experimental identification of process damping model parameters can limit pre-process parameter selection that leverages the potential increases in material removal rates. This paper proposes a physics-informed Bayesian method that can identify the cutting force and process damping model coefficients from a limited set of test cuts without requiring direct measurements of cutting force or vibration. The method uses time-domain simulation to incorporate process damping and provide a basis for test selection. New strategies for efficient sampling and dimensionality reduction are applied to lower computation time and minimize the effect of model error. The proposed method is demonstrated, and the identified cutting and damping force coefficients are compared to values obtained using machining tests and least-squares fitting.

A method for safety analysis of human-machine systems based on dynamic Bayesian simulation

Accidents in human-machine systems often lead to serious consequences, so safety analysis is very important for such systems. However, the existing approach to safety analysis of human-machine systems lacks clear delineation of the coupling relationships between human and machine, or provide quantitative analysis. To address these issues, this paper proposes a method for safety analysis of human-machine systems, utilizing dynamic Bayesian network (DBN) and dynamic fault tree (DFT). The core of this method is the identification of human-machine coupling relationships, proposing 10 types of logical relationships and presenting corresponding DFT logic. Then, a conversion method from DFT to DBN is designed to avoid combinatorial explosion in complex FTA calculations. Based on the DBN model, simulation is conducted using Gibbs sampling, which offers higher computational efficiency. Additionally, a method for importance analysis is devised to identify critical nodes that impact the system risk. At last, a case study of refueling mission at space launch site is given to illustrate how to apply the method. Through simulation analysis, the safety risks during the refueling mission are quantitatively assessed, while critical nodes are identified. The results indicate that the dynamic Bayesian simulation method is good in information utilization, dynamic representation, and time performance.

Bayesian Optimization and Monte Carlo Simulation in Technology Sector Portfolio Allocation: A Comparative Analysis Using the Sharpe Ratio

Bayesian Optimization and Monte Carlo Simulation in Technology Sector Portfolio Allocation: A Comparative Analysis Using the Sharpe Ratio

Review state-of-the-art of output-based methodological approaches for substantiating freedom from infection.

A wide variety of control and surveillance programmes that are designed and implemented based on country-specific conditions exists for infectious cattle diseases that are not regulated. This heterogeneity renders difficult the comparison of probabilities of freedom from infection estimated from collected surveillance data. The objectives of this review were to outline the methodological and epidemiological considerations for the estimation of probabilities of freedom from infection from surveillance information and review state-of-the-art methods estimating the probabilities of freedom from infection from heterogeneous surveillance data. Substantiating freedom from infection consists in quantifying the evidence of absence from the absence of evidence. The quantification usually consists in estimating the probability of observing no positive test result, in a given sample, assuming that the infection is present at a chosen (low) prevalence, called the design prevalence. The usual surveillance outputs are the sensitivity of surveillance and the probability of freedom from infection. A variety of factors influencing the choice of a method are presented; disease prevalence context, performance of the tests used, risk factors of infection, structure of the surveillance programme and frequency of testing. The existing methods for estimating the probability of freedom from infection are scenario trees, Bayesian belief networks, simulation methods, Bayesian prevalence estimation methods and the STOC free model. Scenario trees analysis is the current reference method for proving freedom from infection and is widely used in countries that claim freedom. Bayesian belief networks and simulation methods are considered extensions of scenario trees. They can be applied to more complex surveillance schemes and represent complex infection dynamics. Bayesian prevalence estimation methods and the STOC free model allow freedom from infection estimation at the herd-level from longitudinal surveillance data, considering risk factor information and the structure of the population. Comparison of surveillance outputs from heterogeneous surveillance programmes for estimating the probability of freedom from infection is a difficult task. This paper is a 'guide towards substantiating freedom from infection' that describes both all assumptions-limitations and available methods that can be applied in different settings.

Spatiotemporal Patterns and Drivers of Trade-Offs and Synergy in the Beijing–Tianjin Sand Source Control Project: A Bayesian Belief Network-Based Analysis

Understanding the interactions between ecosystem services is the foundation for optimizing ecosystem management and improving human well-being. However, studies on the driving mechanism of ecosystem service relationship formation in arid and semiarid climates are scarce. The Beijing–Tianjin Sand Source Control Project (BTSSCP) has been underway for more than 20 years (2001–2022), and a comprehensive scientific assessment of the effects of its implementation is important for managing ecosystems more efficiently. Taking the BTSSCP region as a study area, four ecosystem services (water conservation (WC), soil conservation (SC), wind erosion control (WEC), and net primary productivity (NPP)) were quantified and mapped in 2000, 2010, and 2020. In this study, a Bayesian belief network (BBN) model was used to analyze ecological processes and determine the relationship between the potential influencing factors and ecosystem services. A sensitivity analysis identified the key factors affecting ecosystem service supply on the basis of a Bayesian belief network simulation. The results showed an increasing trend for four ecosystem services over the past 20 years. Regarding spatial distribution, WC, SC, and NPP exhibited an overall “high in the east and low in the west” pattern, while the spatial distribution of WEC was more dispersed. The intensity of the trade-offs among WC, SC, and NPP has increased, while that of the trade-offs between the rest of the variables has decreased in the BTSSCP over the past 20 years. The results of the Bayesian network modeling indicated that precipitation, NDVI, land use, and temperature were the major variables influencing the strength of ecosystem service trade-offs. The conditional probabilities of the key variables in different states showed that the Sunit Left Banner, Sunit Right Banner, and other areas of control of the desertification of arid grassland had a high probability of trade-offs in WC_SC and SC_NPP. However, the probability of a trade-off between WEC and NPP was higher in the southeastern part of the Yanshan Hills Mountain Water Source Reserve than in the other regions; thus, it should be prioritized as an area for ecological restoration in future planning. This paper provides a scientific reference for the effective protection of ecosystems and the formulation of sustainable policies.

A preliminary study of genetic diversity in populations of Montrichardia Crueg. (Araceae) estimated with ISSR molecular markers

AbstractThe two species of Montrichardia (Araceae) are perennial herbs of great ecological, economic, and ethnobotanical importance that form populations by clonal growth and sexual reproduction. Here, genetic diversity was studied within a drainage system distant from Amazonia. Five populations were sampled from the Rio Parnaíba Delta (northeast Brazil) and two outside it. The eight ISSR primers selected generated 342 marker bands. The non‐Delta populations (M. arborescens) were grouped together by cluster analysis and Bayesian simulation but the Delta populations (M. linifera) were only grouped weakly. Ordination and Bayesian simulation grouped populations into three pairs along an east–west axis. All population pairs were significantly different (pairwise PhiPT, p ≤ 0.001). Between‐population variance (AMOVA, 39.9% variance, p < 0.001) was much greater than between‐species (12.2%, p < 0.034), but within‐population variance was greatest (48.0%). Within the Delta, geographical distance between populations did not predict genetic similarity, but relative within‐population diversity appears to be influenced by habitat differences. There is genetic evidence, but relatively weak, for recognizing the Delta populations as M. linifera and the non‐Delta ones as M. arborescens; Paulino Neves (Maranhão) appears to be the easternmost record for this species. The strong between‐population differences previously reported for M. linifera are corroborated.

External Evaluation of Population Pharmacokinetic Models for High-Dose Methotrexate in Adult Patients with Hematological Tumors.

Currently, numerous population pharmacokinetic (popPK) models for methotrexate (MTX) have been published for estimating PK parameters and variability. However, it is unclear whether the accuracy of these models is sufficient for clinical application. The aim of this study is to evaluate published models and assess their predictive performance according to the standards of scientific research. A total of 237 samples from 74 adult patients who underwent high-dose MTX (HDMTX) treatment at Shanghai Changzheng Hospital were collected. The software package NONMEM was used to perform an external evaluation for each model, including prediction-based diagnosis, simulation-based diagnosis, and Bayesian forecasting. The simulation-based diagnosis includes normalized prediction distribution error (NPDE) and visual predictive check (VPC). Following screening, 7 candidate models suitable for external validation were identified for comparison. However, none of these models exhibited excellent predictive performance. Bayesian simulation results indicated that the prediction precision and accuracy of all models significantly improved when incorporating prior concentration information. The published popPK models for MTX exhibit significant differences in their predictive performance, and none of the models were able to accurately predict MTX concentrations in our data set. Therefore, before adopting any model in clinical practice, extensive evaluation should be conducted.

"Extended Descriptive Risk-Averse Bayesian Model" a More Comprehensive Approach in Simulating Complex Biological Motion Perception.

The ability to perceive biological motion is crucial for human survival, social interactions, and communication. Over the years, researchers have studied the mechanisms and neurobiological substrates that enable this ability. In a previous study, we proposed a descriptive Bayesian simulation model to represent the dorsal pathway of the visual system, which processes motion information. The model was inspired by recent studies that questioned the impact of dynamic form cues in biological motion perception and was trained to distinguish the direction of a soccer ball from a set of complex biological motion soccer-kick stimuli. However, the model was unable to simulate the reaction times of the athletes in a credible manner, and a few subjects could not be simulated. In this current work, we implemented a novel disremembering strategy to incorporate neural adaptation at the decision-making level, which improved the model's ability to simulate the athletes' reaction times. We also introduced receptive fields to detect rotational optic flow patterns not considered in the previous model to simulate a new subject and improve the correlation between the simulation and experimental data. The findings suggest that rotational optic flow plays a critical role in the decision-making process and sheds light on how different individuals perform at different levels. The correlation analysis of human versus simulation data shows a significant, almost perfect correlation between experimental and simulated angular thresholds and slopes, respectively. The analysis also reveals a strong relation between the average reaction times of the athletes and the simulations.

Is it time to recommend AUC-based vancomycin therapeutic drug monitoring only? A cross-sectional survey in China.

The latest published therapeutic drug monitoring (TDM) guidelines for vancomycin recommend changing trough-based monitoring to area under the concentration-to-time curve (AUC)-based monitoring. This study aimed to evaluate the implementation status and perceptions of vancomycin AUC-based TDM in China and to determine the challenges in performing AUC-based TDM. A nationwide cross-sectional survey was conducted in China using an online questionnaire. The questionnaire comprised a total of 25 questions with open- and closed-ended answers to collect information about the current implementation of vancomycin TDM and the participants' perceptions of these practices. The questionnaire responses were collected via the Questionnaire Star platform and analyzed. A total of 161 questionnaires were completed by 131 hospitals and were included. Approximately 59.5% (78/131) of the surveyed hospitals conducted vancomycin TDM; however, only 10.7% (14/131) of these hospitals performed AUC-based vancomycin TDM. Of the eligible participants, 58.4% (94/161) had experience with vancomycin TDM, and only 37 participants (37/161, 23.0%) had the ability to estimate the AUC, primarily through Bayesian simulation (33/161, 20.5%). The participants considered the following challenges to implementing AUC-based monitoring: (1) the high cost of AUC-based monitoring; (2) inadequate knowledge among pharmacists and/or physicians; (3) the complexity of AUC calculations; (4) difficulty obtaining AUC software; and (5) unclear benefit of AUC-based monitoring. The majority of surveyed hospitals have not yet implemented AUC-based vancomycin TDM. Multiple challenges should be addressed before wide implementation of AUC-based monitoring, and guidance for trough-based monitoring is still needed.

Efficacy of anti-obesity agents: a systematic review and network meta-analysis of randomized controlled trials

Abstract Background: To conduct a network meta-analysis comparing the safety and efficacy of five anti-obesity drugs approved by the United States Food and Drug Administration (US FDA)-Bupropion/Naltrexone combination (BUP/NLX), Liraglutide (LIRA), Orlistat (ORLI), Phentiramine/Topiramate combination (PHEN/TPM) and Simaglutide (SGT) vs placebo. Methods: The study's eligibility criteria include randomized controlled trials (RCTs) with a focus on obese patients receiving BUP/NLX or LIRA or ORLI or PHEN/TPM or SGT versus placebo. We conducted a comprehensive search of electronic databases (PubMed, Embase, Cochrane Library, and Scopus) to identify relevant randomized controlled trials published, with no restrictions on the publication language or year. Three reviewers independently screened the studies, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias tool. Bucher's and Bayesian Meta-regression Simulation Method were used for indirect head-to-head comparison between various active drugs. RevMan Version 5.4® along with A Network Meta- Analysis Toolkit by Cochrane Methods were used. p-value less than 0.05 was considered significant. Results: Total 28 studies were included in this meta-analysis. PHEN/TPM combination exhibited (odds ratio :0.568, p value <0.001, cl 95%) ORLI (odds ratio: 0.889, p value <0.001, cl 95%), SGT (odds ratio: 0.922, p value <0.001, cl 95%). BUP/NLX combination exhibited a high (odds ratio: 4.61, p value <0.001, cl 95%) LIRA displayed the lowest (odds ratio: 1.109, p value <0.001, cl 95%). Network meta-analysis revealed. BUP/NLX combination exhibited highest Efficacy. ORLI found as safest among the evaluated drugs. SGT had significant likelihood of adverse events (odds ratio = 1.328, p-value<0.0001, Cl 95%) compared to ORLI (odds ratio = 0.138, p-value<0.0001, Cl 95%), BUP/NLX (odds ratio = 0.197, p-value<0.0001, cl 95%), and LIRA (odds ratio = 0.456, p-value < 0.001, Cl 95%), PHEN/TPM (odds ratio = 0.456, p-value<0.0001, Cl 95%). Discussion: These findings have important clinical implications for the management of obesity. The BUP/NLX , LIRA , and SGT can be considered as effective treatment options for weight reduction. However, healthcare providers need to carefully consider the safety profiles and potential side-effects of these medications when making treatment decisions. The study relied on aggregated data, which might introduce bias. High attrition rates and heterogeneity among studies limit the findings. It only compared common gastrointestinal side effects and didn't use the GRADE approach for evidence quality. Conclusion: Study provides evidence supporting the efficacy of anti-obesity medications compared to placebo. BUP/NLX combination, LIRA, and SGT emerged as the most effective agents, considering safety profile. Findings can guide clinicians about options for obesity management. Study Registration: The study is registered with PROSPERO (CRD42023465989).

A lingering legacy of leaded gasoline in Southeast Asia

Recent reports of 1 in 3 children globally having blood lead levels ≥ 5microgram decilitre−1 demands thorough understanding of lead (Pb) sources of the present century and the fate of legacy Pb from the past use of leaded gasoline. The present hotspot of pollution is South and Southeast Asia. To investigate this issue, here we compile Pb isotopic compositions of aerosols (n = 341) along with established and previously excluded sources for Singapore, Thailand, Vietnam, and India. The data was subjected to Bayesian 3D isotope mixing model simulation. Model estimates reveal consistent contributions from natural background. Leaded gasoline is the largest contributor in Southeast Asia (39%). Tertiary coal/fuelwood combustion and ore processing dominate in India, while ship emission contribute up to 15%. Thus, along with Pb from present sources, the historic use of leaded gasoline left a legacy of Pb in soil which is remobilised to the atmosphere after more than two decades of its phase-out.

A Quantitative Analysis of Decision-Making Risk Factors for Mega Infrastructure Projects in China

The “trillion-dollar era” of megaprojects has increased the demand for the scope of mega infrastructure. To address the requirement for high-quality “investment, construction, and operation” integration, the EPC and PPP models must be combined. The complexity of megaprojects has resulted in the complexity of project risk variables under the new model. However, few existing studies have undertaken in-depth studies on the risk of EPC + PPP megaprojects. The interplay and dynamic evolution of risk factors, in particular, have not been taken into account. This research intends to fill this gap by systematically identifying and modeling the risk variables associated with the adoption of the EPC + PPP model for mega infrastructure projects. In this study, the Bayesian network is used to detect decision-making risk variables for large infrastructure projects in China. The findings indicate that (i) 22 influencing factors of megaproject decision making are identified, including organizational decision making, PPP investment and financing, EPC construction, operations management, and policy and law. (ii) Considering the real project decision-making process, a model based on a dynamic Bayesian network is built, and associated governance techniques and early warning protection mechanisms are designed for the decision-making process. (iii) Using the Yiwu Mall Avenue project as an example, the Bayesian simulation model of decision-making risks is applied to a typical case to validate its feasibility and correctness. These findings have significant theoretical and practical implications for research on the identification and governance of decision-making risks in megaprojects using the EPC + PPP model in China.

Quantifying the Nitrogen Sources and Secondary Formation of Ambient HONO with a Stable Isotopic Method.

Nitrous acid (HONO) is a reactive gas that plays an important role in atmospheric chemistry. However, accurately quantifying its direct emissions and secondary formation in the atmosphere as well as attributing it to specific nitrogen sources remains a significant challenge. In this study, we developed a novel method using stable nitrogen and oxygen isotopes (δ15N; δ18O) for apportioning ambient HONO in an urban area in North China. The results show that secondary formation was the dominant HONO formation processes during both day and night, with the NO2 heterogeneous reaction contributing 59.0 ± 14.6% in daytime and 64.4 ± 10.8% at nighttime. A Bayesian simulation demonstrated that the average contributions of coal combustion, biomass burning, vehicle exhaust, and soil emissions to HONO were 22.2 ± 13.1, 26.0 ± 5.7, 28.6 ± 6.7, and 23.2 ± 8.1%, respectively. We propose that the isotopic method presents a promising approach for identifying nitrogen sources and the secondary formation of HONO, which could contribute to mitigating HONO and its adverse effects on air quality.

Isotopic comparison of ammonium between two summertime field campaigns in 2013 and 2021 at a background site of North China

Ammonia (NH3) is the primary atmospheric alkaline gas, playing a crucial role in the atmospheric chemistry. Recently, non-agricultural emissions have been identified as the dominant sources of NH3 in urban areas. However, few studies have quantified the contributions of different sources to regional NH3. This study conducted two summertime field observations in 2013 and 2021 at a background site of North China to comprehensively explore the regional variations in concentration, nitrogen isotope composition (δ15N), and sources of ammonium (NH4+). The results indicate that NHx (NHx = NH3 + NH4+) concentration has increased in 2021, but the fNH4+ (NH4+/ NHx) has decreased significantly. The δ15N-NH4+ values show a significant increase, ranging from −4.7 ± 8.1 ‰ to +12.0 ± 2.4 ‰. The increase can be attributed to two primary factors: changes in fNH4+ resulting from the reduction of atmospheric acid gases and alterations in the sources of NH3. Bayesian simulation analysis reveals substantial variations in NH3 sources between 2013 and 2021 observations. Non-agricultural sources have significantly increased their contribution to NHx concentration, with vehicle exhaust and NH3 slip experiencing growth rates of 187 % and 104 %, respectively. Our results confirm the dominate contribution of non-agricultural sources to regional NH3 at the present stage and propose relevant mitigation strategies, which would provide essential insights for reducing NH3 emissions in North China.

A Source Mechanism of the Mining-Triggered Tremor in the Xinjulong Coal Mine Revealed by the Bayesian Inversion and 3D Simulation

A Source Mechanism of the Mining-Triggered Tremor in the Xinjulong Coal Mine Revealed by the Bayesian Inversion and 3D Simulation

Multimessenger parameter inference of gravitational-wave and electromagnetic observations of white dwarf binaries

ABSTRACT The upcoming Laser Interferometer Space Antenna (LISA) will detect a large gravitational-wave foreground of Galactic white dwarf binaries. These sources are exceptional for their probable detection at electromagnetic wavelengths, some long before LISA flies. Studies in both gravitational and electromagnetic waves will yield strong constraints on system parameters not achievable through measurements of one messenger alone. In this work, we present a Bayesian inference pipeline and simulation suite in which we study potential constraints on binaries in a variety of configurations. We show how using LISA detections and parameter estimation can significantly improve constraints on system parameters when used as a prior for the electromagnetic analyses. We also provide rules of thumb for how current measurements will benefit from LISA measurements in the future.