Results 111 to 120 of about 527,420 (298)
DPpackage: Bayesian Semi- and Nonparametric Modeling in R [PDF]
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model.
Fernando A. Quintana +4 more
core
Bayesian semiparametric multi-state models [PDF]
, 2008 Multi-state models provide a unified framework for the description of the evolution of discrete phenomena in continuous time. One particular example is Markov processes which can be characterised by a set of time-constant transition intensities between ...
Thomas Kneib +3 more
core +1 more source
Clinical Pharmacology &Therapeutics, EarlyView.Traditional dosing strategies often rely on a “one‐size‐fits‐all” paradigm, assuming an “average” patient with typical demographic and pharmacological characteristics. In reality, this often overlooks existing between‐patient variability and can lead to suboptimal drug exposure or toxicity. This issue is especially pronounced in pediatric patients, who
Zachary L. Taylor +12 more
wiley +1 more source
Bayesian curve estimation by model averaging [PDF]
, 2003 A bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian Model Averaging (BMA); second, the ...
Peña, Daniel, Redondas, María Dolores
core +1 more source
BMC Bioinformatics, 2017 Background Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results.
Brian Connolly +4 more
doaj +1 more source
Rates of convergence for the posterior distributions of mixtures of Betas and adaptive nonparametric estimation of the density. [PDF]
In this paper, we investigate the asymptotic properties of nonparametric Bayesian mixtures of Betas for estimating a smooth density on [0, 1]. We consider a parametrization of Beta distributions in terms of mean and scale parameters and construct a ...
Rousseau, Judith
core
Considerations for the Integration of Clinical Trials and Real‐World Data
Clinical Pharmacology &Therapeutics, EarlyView.As clinical decision‐making increasingly moves toward individualized and context‐specific treatment recommendations, reliance on any single evidence source, randomized or observational, may be insufficient. Principled integration of randomized controlled trials and real‐world data, grounded in explicit causal frameworks, offers a path toward evidence ...
Sky Qiu +19 more
wiley +1 more source
Bayesian Nonparametric Kernel-Learning
, 2015 Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approximating kernel is selected, the quality of the finite sample estimator may be greatly affected by the choice of kernel.
Junier B. Oliva +5 more
openaire +3 more sources
On Nonparametric Predictive Inference and Objective Bayesianism [PDF]
Journal of Logic, Language and Information, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +4 more sources