- Referenced in 64 articles
- BayesTree: Bayesian Methods for Tree Based Models: Implementation of BART: Bayesian Additive Regression Trees ... Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by ensemble methods...
- Referenced in 236 articles
- learning, graphical models, and Bayesian statistics (hence the logo). (Some methods from frequentist statistics, such...
- Referenced in 378 articles
- Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. The project...
- Referenced in 47 articles
- variety of frequentist and Bayesian methods. It is also a platform for the implementation...
- Referenced in 389 articles
- geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves ... computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem...
- Referenced in 26 articles
- books by Jeff Gill , Books is ”Bayesian Methods: A Social and Behavioral Sciences Approach, Second...
- Referenced in 66 articles
- business students. We discuss our experiences teaching Bayesian statistics to students in doctoral programs ... statistics and a predisposition against likelihood-based methods stemming from prior exposure to econometrics. This ... course that emphasizes the value of the Bayesian approach to solving nontrivial problems. The success ... bayesm, which provides efficient implementation of advanced methods and models...
- Referenced in 37 articles
- using a state-of-the-art Bayesian optimization method. Our new package is tightly integrated...
- Referenced in 34 articles
- estimators. We consider three types of estimation methods: (a) as a standard (fixed effect) nonparametric ... random effects, and (c) a full Bayesian approach. The volume-of-tube formula ... applied for the first two methods and compared with Bayesian simultaneous confidence bands from ... either Bayesian or frequentist properties. Simulations and data analysis support the proposed methods...
- Referenced in 26 articles
- BioBayes: a software package for Bayesian inference in systems biology. MOTIVATION: There are several levels ... which cannot be observed directly. The methods of Bayesian inference provide a consistent framework...
- Referenced in 30 articles
- Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models ... univariate and multivariate data. The main methods and models are illustrated with examples based ... Bayesian analysis for linear regression models are reminded, and Markov chain Monte Carlo methods ... Bayesian inference. The last chapter is on sequential Monte Carlo methods...
- Referenced in 83 articles
- Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by ensemble methods...
- Referenced in 12 articles
- algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented...
- Referenced in 17 articles
- Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization...
- Referenced in 18 articles
- paper, we introduce a novel and efficient Bayesian framework for Gaussian graphical model determination which ... theory and computational details of the method. It is easy to implement and computationally feasible ... dimensional graphs. We show our method outperforms alternative Bayesian approaches in terms of convergence, mixing ... learning. We illustrate the efficiency of the method on a broad range of simulated data...
- Referenced in 60 articles
- Phylogeny. MrBayes is a program for Bayesian inference and model choice across a wide range ... MrBayes uses Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model...
- Referenced in 38 articles
- rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application...
- Referenced in 11 articles
- HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. The diffusion model ... model to data, current estimation methods require an abundance of response time measurements to recover ... each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power ... subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers...
- Referenced in 21 articles
- monotonic nonparametric regression. Implements the Bayesian nonparametric monotonic regression method described in Bornkamp & Ickstadt...
- Referenced in 5 articles
- population structure inference via maximum likelihood method. Background: Inference of population stratification and individual admixture ... association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture ... widely used in practice. However, these Bayesian methods demand intensive computation resources ... Conclusion: Compared with software based on Bayesian methods (e.g., STRUCTURE), PSMIX has similar accuracy...