27 papers · ranked by Valyu relevance
Nithisha Suryadevara, Vivek Reddy Srigiri
— Joint modeling of longitudinal and survival data has become increasingly important in medical research, particularly for understanding disease progression in chronic conditions where both repeated biomarker measurements and time-toevent outcomes are available. Traditional two-stage methods, which analyze longitudinal…
Carmen Armero
This paper takes a quick look at Bayesian joint models (BJM) for longitudinal and survival data. A general formulation for BJM is examined in terms of the sampling distribution of the longitudinal and survival processes, the conditional distribution of the random effects and the prior distribution. Next a basic BJM…
Yangxin Huang, Jiaqing Chen, Lan Xu, Nian-Sheng Tang
Joint models of longitudinal and time-to-event data have received a lot of attention in epidemiological and clinical research under a linear mixed-effects model with the normal assumption for a single longitudinal outcome and Cox proportional hazards model. However, those model-based analyses may not provide robust…
Felix M. Pabon-Rodriguez, Grant D. Brown, Breanna M. Scorza, Christine A. Petersen
The host immune system plays a significant role in managing and clearing pathogen material during an infection, but this complex process presents numerous challenges from a modeling perspective. There are many mathematical and statistical models for these kinds of processes that take into account a wide range of events…
Lu Zhang, Sudipto Banerjee, Andrew O. Finley
Joint modeling of spatially oriented dependent variables is commonplace in the environmental sciences, where scientists seek to estimate the relationships among a set of environmental outcomes accounting for dependence among these outcomes and the spatial dependence for each outcome. Such modeling is now sought for…
Jie Pu, Di Fang, Jeffrey R. Wilson
Background The analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random component. Since the joint distribution of the observations is…
Meike Köhler, Nikolaus Umlauf, Sonja Greven
Joint models of longitudinal and survival data have become an important tool for modeling associations between longitudinal biomarkers and event processes. The association between marker and log-hazard is assumed to be linear in existing shared random effects models, with this assumption usually remaining unchecked. We…
Verrah A. Otiende, Thomas N. Achia, Henry G. Mwambi, Eric Forgoston
The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine…
Alan E. Gelfand, Shinichiro Shirota
Joint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected dependence/interaction between species. These models attempt to capture species dependence through an associated correlation matrix arising from a…
Mingyang Cai, Stef van Buuren, Gerko Vink
Fully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables. However, FCS may not converge to a stationary distribution. Many authors have studied the convergence…
Gilles Kratzer, Fraser I. Lewis, Barbara Willi, Marina L. Meli + 5 more
'Felicitas S. Boretti' 'Regina Hofmann-Lehmann' 'Paul Torgerson' 'Reinhard Furrer' 'Sonja Hartnack'] Bayesian network (BN) modeling is a rich and flexible analytical framework capable of elucidating complex veterinary epidemiological data. It is a graphical modeling technique that enables the visual presentation of…
Karine Tung, Steven De La Torre, Mohamed El Mistiri, Rebecca Braga De Braganca + 6 more
'Rebecca Braga De Braganca' 'Eric B. Hekler' 'Misha Pavel' 'Daniel E. Rivera' 'Pedja Klasnja' 'Donna Spruijt‐Metz' 'Benjamin M. Marlin'] In this paper we present BayesLDM, a system for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex…
Chong Wang, David M. Blei
Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and…
Zhixiao Zhu, Maria Christodoulou, David Steinsaltz
Many complex systems are modelled using modular models, where individual sub-models are estimated separately and then combined. While this simplifies inference, it fails to account for interactions between components. A natural solution is to estimate all components jointly, but this is often impractical due to…
Qingyang Zhang, Xuan Shi
Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when normality assumption is moderately…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…
Max Hinne
Bayesian inference is becoming an increasingly popular framework for statistics in the behavioral sciences. However, its application is hampered by its computational intractability - almost all Bayesian analyses require a form of approximation. While some of these approximate inference algorithms, such as Markov chain…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Mark A. Gannon
Students are told in basic probability classes that there are two main “schools” of statistics, the frequentist and the Bayesian, and that those different views of how to approach statistical inference problems arise from two different views of the meaning of probability. Practicing scientists know things are not that…
Sebastian Sosa, Mary Brooke McElreath, Cody Ross
Bayesian modeling is a cornerstone of modern ecological and evolutionary research, offering the flexibility to account for hierarchical structures, imperfect detection, and spatial dependencies. However, as ecological datasets grow in scale and complexity—from high-resolution telemetry to phylogenomics—researchers…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…