25 papers · ranked by Valyu relevance
Yue Yu, Daniel Simpson, Finn Lindgren, Håvard Rue
The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. However, there are two obstacles that restrict the use of smoothing spline in practical statistical work. Firstly, it…
Andreas Heinecke, Marta Tallarita, Maria De Iorio
Background Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and…
Peter Enesi Omaku, Joseph Odunayo Braimah, Fabio Mathias Correa
Introduction The Cox proportional hazards (PH) model is widely used in time-to-event research, but its validity depends on the PH assumption, which can be violated in child mortality studies where hazards vary with age. Piecewise exponential models (PEMs) relax this assumption by partitioning follow-up time into…
Muhammad Abu Shadeque Mullah, James A. Hanley, Andrea Benedetti
Background Generalized linear mixed models (GLMMs), typically used for analyzing correlated data, can also be used for smoothing by considering the knot coefficients from a regression spline as random effects. The resulting models are called semiparametric mixed models (SPMMs). Allowing the random knot coefficients to…
Oswaldo Gressani, Paul H.C. Eilers
P-splines are appealing for smoothing Poisson distributed counts. They provide a flexible setting for modeling nonlinear model components based on a discretized penalty structure with a relatively simple computational backbone. Under a Bayesian inferential process relying on Markov chain Monte Carlo, estimates of…
Vaanathi Sundaresan, Ludovica Griffanti, Petya Kindalova, Fidel Alfaro-Almagro + 4 more
White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and…
Sunwoo Lim, Seonghyun Jeong
Bayesian P-splines and basis determination through Bayesian model selection are both commonly employed strategies for nonparametric regression using spline basis expansions within the Bayesian framework. Despite their widespread use, each method has particular limitations that may introduce potential estimation bias…
Massimo Ventrucci, Håvard Rue
Bayesian P-splines assume an intrinsic Gaussian Markov random field prior on the spline coefficients, conditional on a precision hyper-parameter τ . Prior elicitation of τ is difficult. To overcome this issue we aim to building priors on an interpretable property of the model, indicating the complexity of the smooth…
Ian Wadsworth, Lisa V Hampson, Björn Bornkamp, Thomas Jaki
Within paediatric populations, there may be distinct age groups characterised by different exposure-response relationships. Several regulatory guidance documents have suggested general age groupings. However, it is not clear whether these categorisations will be suitable for all new medicines and in all disease areas.…
Zhanglong Cao, David Bryant, Matthew Parry
It is known that a smoothing spline can be thought of as the posterior mean of a Gaussian process regression in a certain limit. By constructing a reproducing kernel Hilbert space with an appropriate inner product, the Bayesian form of the V-spline is derived when the penalty term is a fixed constant instead of a…
Priyam Das, Subhashis Ghosal
We consider a Bayesian method for simultaneous quantile regression on a real variable. By monotone transformation, we can make both the response variable and the predictor variable take values in the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing…
Kushani De Silva, Carlo Cafaro, Adom Giffin, Antonio M. Scarfone
Attaining reliable gradient profiles is of utmost relevance for many physical systems. In many situations, the estimation of the gradient is inaccurate due to noise. It is common practice to first estimate the underlying system and then compute the gradient profile by taking the subsequent analytic derivative of the…
Ziren Jiang, Ciprian Crainiceanu, Erjia Cui
This manuscript provides step-by-step instructions for implementing Bayesian functional regression models using Stan. Extensive simulations indicate that the inferential performance of the methods is comparable to that of state-of-the-art frequentist approaches. However, Bayesian approaches allow for more flexible…
David L. Miller
Links between frequentist and Bayesian approaches to smoothing were highlighted early on in the smoothing literature, and power much of the machinery that underlies the modern generalized additive modelling framework (implemented in software such as the R package mgcv), but they tend to be unknown or under appreciated.…
Abelardo Montesinos-López, Osval A. Montesinos-López, Gustavo de los Campos, José Crossa + 2 more
'Gustavo de los Campos' 'José Crossa' 'Juan Burgueño' 'Francisco Javier Luna-Vazquez'] Background Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1-23, [17].…
Mengfan Li, Wei Shi, Sheng Liu, Shuang Fu + 3 more
The recent development of single molecule imaging techniques has enabled not only high accuracy spatial resolution imaging but also information rich functional imaging. Abundant information of the single molecules can be encoded in its diffraction pattern and be extracted precisely (e.g. 3D position, wavelength, dipole…
Jungin Choi, Abhirup Datta, Martin A. Lindquist
Task-based fMRI is commonly analyzed using voxel-wise general linear models, a non-spatial scalable approach that can yield fragmented activation maps. Spatial alternatives such as kernel smoothing and Bayesian models address this but either blur activation boundaries or are computationally prohibitive at modern…
Mehrad Sarmashghi, Shantanu P Jadhav, Uri Eden
Point process generalized linear models (GLMs) provide a powerful tool for characterizing the coding properties of neural populations. Spline basis functions are often used in point process GLMs, when the relationship between the spiking and driving signals are nonlinear, but common choices for the structure of these…
Lorenzo Contento, Paul Stapor, Daniel Weindl, Jan Hasenauer
Spline interpolants are commonly used for discretizing and estimating functions in mathematical models. While splines can be encoded in the Systems Biology Markup Language (SBML) using piecewise functions, the resulting formulas are very complex and difficult to derive by hand. Tools to create such formulas exist but…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
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…
Jonas Verhellen
In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 12, 3567-3572] have, however, been shown to rival deep learning…
Authors not listed
Thorough treatment of conformation in computational chemistry is required to capture the subtle energy differences that lead to experimental observations. Accurate quantum chemistry calculations are very expensive and evaluation of the entire ensemble found during a conformational search is often unachievable. This is…