... estimation accuracy for the modified ETAS model, substantiating its potential as a robust tool in seismicity analysis. Spatio-temporal seismicity modelling ETAS model Statistical seismology Earthquake forecasting Bayesian inference O RIGIN AL U N ED ITED M A N U SCRIPT submitted to Geophys. J. Int...

... and in an application to epidemiology, CLIC accurately characterizes view-specific partitions while providing inference on the dependence level. Bayesian inference Bayesian nonparametrics mixture models multiview clustering random partitions National Institutes of Health 10.13039/100000002 1R01AI155733...

... relative quiet and restful sleep compared with male infants. fMRI brain development modularity Bayesian inference China Ministry of Science and Technology 2022ZD02090 00 S20240085 2022ZD0213100 National Natural Science Foundation of China 10.13039/501100001809 U23A20295 82441023 62131015...

..., the B-spline model generally improves out-of-sample predictions. The case studies suggest that the BTM may be considered for demographic applications. Bayesian inference B-splines demography global health time series Bill & Melinda Gates Foundation 10.13039/100000865 INV-00844 Projections...

... an empirical probabilistic relationship between the parameters and data, without making assumptions about the data errors. This is achieved through the use of specialized machine learning models, known as neural density estimators, which can then be integrated into the Bayesian inference framework. We apply...

... and interpretability of predictors. In addition, this family of models defines the mixing probabilities through a logistic regression, which proved to be particularly efficient in our applied setting, as demonstrated by a simulation study. Bayesian inference DHS survey Fay–Herriot model wellbeing indicators CUP...

... Standard Bayesian inference enables building models that combine information from various sources, but this inference may not be reliable if components of the model are misspecified. Cut inference, a particular type of modularized Bayesian inference, is an alternative that splits a model into modules...

... as continuous ground motion data are available at the target site. Bayesian inference Probabilistic forecasting Earthquake hazards Earthquake interaction, forecasting, and prediction Statistical seismology Japan Society for the Promotion of Science 10.13039/501100001691 JPJ010217 21K03686 The Omori...

... underestimated in 10 out of 20 countries and significantly overestimated in 5 out of 20. Bayesian inference case-control study global public health hierarchical model infectious disease test-negative design vaccination coverage ACCEPTED MANUSCRIPT American Journal of Epidemiology Submitted Manuscript1...

... with models with a transitory shock effect. Bayesian inference jump effects pandemic shocks stochastic mortality modelling Oberfrankenstiftung FP01054 The model of Lee and Carter (1992) has been widely used in actuarial science and demography to forecast mortality rates based on past observations...

... household transmission data. bayesian inference mathematical model RSV transmission RSV viral load kinetics Respiratory syncytial virus (RSV) infections pose a significant public health threat to pediatric populations, older adults, and individuals who are immunocompromised [ 1 ]. In 2019, an estimated...

... against their competing objectives of preserving financial stability and restraining moral hazard? bank failures Bayesian inference collapsed Gibbs sampler Federal Deposit Insurance Corporation (FDIC) Federal Savings and Loans Insurance Corporation (FSLIC) latent class models Abstract When banks...

...Fernando A S Moura; Soraia Pereira; Giovani L Silva Area level model Hierarchical model Bayesian inference MCMC methods Location and scale modeling Fundação para a Ciência e Tecnologia Address correspondence to Fernando A. S. Moura, Statistics Department of the Federal University of Rio de...

... fine mantle stratification imaging using SHARP-SS at locations where seismic stations are sparsely distributed. Composition and structure of the mantle Bayesian inference Body waves Seismic discontinuities National Science Foundation 10.13039/100000001 EAR-1261681 2339370 2102495...

.... Bayesian inference causal inference double robustness semiparametric theory Novo Nordisk 10.13039/501100004191 Alan Turing Institute 10.13039/100012338 Li Ka Shing Foundation 10.13039/100007421 Medical Research Council 10.13039/501100000265 EPSRC 10.13039/501100000266...

...Sean Berti; Matteo Ravasi; Mattia Aleardi; Eusebio Stucchi In recent years, variational inference (VI) has been introduced to geophysics as an alternative to MCMC techniques, offering improved efficiency for Bayesian inference in certain types of problems. Variational methods define a family...

... that EKI provides a reliable and efficient means of obtaining accurate parameter estimates for large-scale, two-phase geothermal reservoir models, with appropriate characterization of uncertainty. Bayesian inference Inverse theory Numerical modelling Probabilistic forecasting Hydrothermal systems...

... inherit computational advantages and stability from the combination of Occam’s inversion, split Bregman and RTO, and, therefore, can be expected to be robustly applicable across geophysics. Marine electromagnetics Bayesian inference Inverse theory NSF 10.13039/100000001 EAR-2433476 ONR...

... the relationship between faulting and seismicity. In addition, the new method provides a quantitative measure of the uncertainty in the velocity which cannot be obtained using a traditional method. In Bayesian inference, the prior information is represented as a probability density function (pdf), which...

... this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement. Bayesian inference Statistical methods...