Results 11 to 20 of about 23,661,782 (208)

Bayesian Approximations to Hidden Semi-Markov Models for Telemetric Monitoring of Physical Activity [PDF]

, 2022
We propose a Bayesian hidden Markov model for analyzing time series and sequential data where a special structure of the transition probability matrix is embedded to model explicit-duration semi-Markovian dynamics.
Fiecas, Mark, Jewson, Jack, Hadj-Amar, Beniamino, Mark Fiecas, Jack Jewson, Beniamino Hadj-Amar   +5 more
core   +1 more source

Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity [PDF]

, 2023
Simulation-based calibration checking (SBC) is a practical method to validate computationally-derived posterior distributions or their approximations. In this paper, we introduce a new variant of SBC to alleviate several known problems.
Shinyoung, Kim, Moon, Angie H., Bürkner, Paul, Gelman, Andrew, Kim, Shinyoung, Burkner, Paul-Christian, Modrák, Martin, Faltejsková, Kateřina, Huurre, Niko, Vehtari, Aki   +9 more
core   +1 more source

Multiscale Bayesian survival analysis [PDF]

The Annals of Statistics, 2021
We consider Bayesian nonparametric inference in the right-censoring survival model, where modeling is made at the level of the hazard rate. We derive posterior limiting distributions for linear functionals of the hazard, and then for `many' functionals simultaneously in appropriate multiscale spaces.
Castillo, Ismaël, van der Pas, Stéphanie   +1 more
openaire   +5 more sources

Bayesian Analysis of Isochores [PDF]

Journal of the American Statistical Association, 2009
Statistical identification of isochore structure, the variation in large-scale GC composition (proportion of DNA bases that are G or C as opposed to A or T), of mammalian genomes is a necessary requirement for understanding both the evolution of base composition and the many genomic features such as mutation and recombination rates, which covary with ...
Fearnhead, Paul, Vasilieou, Despina
openaire   +2 more sources

Informed Bayesian survival analysis

BMC Medical Research Methodology, 2022
AbstractBackgroundWe provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of ...
Bartoš, F., Aust, F., Haaf, J.M.
openaire   +6 more sources

Efficient utility-based clustering over high dimensional partition spaces [PDF]

, 2009
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ, Millar, A. J., Millar, AJ, Liverani, Silvia, Smith, J. Q., Liverani, S, Anderson, Paul E., Anderson, PE, Edwards, Kieron D., Edwards, KD   +9 more
core   +1 more source

BAT – The Bayesian analysis toolkit [PDF]

Computer Physics Communications, 2009
We describe the development of a new toolkit for data analysis. The analysis package is based on Bayes' Theorem, and is realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution. Parameter estimation, limit setting and uncertainty propagation are implemented in a straightforward manner.
Allen Caldwell, Daniel Kollár, Kevin Kröninger   +2 more
openaire   +5 more sources

Using stacking to average bayesian predictive distributions (with discussion) [PDF]

, 2018
Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of ...
Grunwald, Peter, Castro-Camilo, Daniela, Li, Meng, Shin, Minsuk, Grushka-Cockayne, Yael, Gelman, Andrew, Lichtendahl, Kenneth C., Iacopini, Matteo, Dawid, A. Philip, van Dijk, Herman K., Lichtendahl, Kenneth C, Yao, Yuling, Zhou, Tianjian, Winkler, Robert L, Aastveit, Knut Are, Dawid, A Philip, Yoo, William Weimin, Pericchi, Luis, Clarke, Bertrand, Bakka, Haakon C, Freni-Sterrantino, Anna, Franck, Christopher T., Hoogerheide, Lennart, Tonellato, Stefano, Winkler, Robert L., Simpson, Daniel, Ferreira, Marco A. R., Franco-Villoria, Maria, Belitser, Eduard, Bakka, Haakon C., van Dijk, Herman K, Ferreira, Marco AR, de Heide, Rianne, Nurushev, Nurzhan, Jose, Victor Richmond R., Vehtari, Aki, Clyde, Merlise, McAlinn, Kenichiro, West, Mike, Rue, Havard, Huser, Raphael, Opitz, Thomas, Jose, Victor Richmond R, Franck, Christopher T   +43 more
core   +2 more sources

Spatial 3D Matérn Priors for Fast Whole-Brain fMRI Analysis

, 2021
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors has been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Lindgren, Finn,, Bolin, David, Eklund, Anders,, Bolin, David,, Sidén, Per, Eklund, Anders, Villani, Mattias, Sidén, Per,, Lindgren, Finn, Villani, Mattias,, David Bolin, Finn Lindgren, Mattias Villani, Per Sidén, Anders Eklund   +14 more
core   +1 more source

Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths [PDF]

, 2022
The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States.
Wang, Yuanrong, Jogarah, Vidoushee, Cardoso Correia Perello, Carlos, Bhatt, Samir, Flaxman, Seth, Ratmann, Oliver, Monod, Melodie, Chen, Yu, Blenkinsop, Alexandra, Brizzi, Andrea   +9 more
core   +1 more source