Aki Vehtari
Datos Biográficos
| ID | 7753566 |
|---|---|
| NOMBRE | Aki Vehtari |
| NOMBRES | Aki |
| APELLIDO | Vehtari |
| FIRMA | VEHTARI A |
| AFILIACIONES | Aalto University |
| ORCID | 0000-0003-2164-9469 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 9 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 9 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2009 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2021 |
| ÍNDICE H | 0 |
Rank-Normalization, Folding, and Localization
Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic Rˆ of Gelman and Rubin (1992) has serious flaws. Traditional Rˆ will fail to correctly diagnose convergence failures when the chain has a heavy tail or when the variance varies across the chains. In this paper we propose an alte…
R-squared for Bayesian Regression Models
The usual definition of R2 (variance of the predicted values divided by the variance of the data) has a problem for Bayesian fits, as the numerator can be larger than the denominator. We propose an alternative definition similar to one that has appeared in the survival analysis literature: the variance of the predicted values divided by the variance of predicted values plus the expected variance of the errors.
Visualization in Bayesian Workflow
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from …
Effectiveness of three interventions for secondary prevention of low back pain in the occupational health setting - a randomised controlled trial with a natural course control
Number NCT00908102 Clinicaltrials.gov
Practical Bayesian model evaluation using leave-one-out cross-validation and Waic
Cost-effectiveness of providing patients with information on managing mild low-back symptoms in an occupational health setting
ClinicalTrials.gov NCT00908102
Understanding predictive information criteria for Bayesian models
The effectiveness of physical activity monitoring and distance counseling in an occupational setting – Results from a randomized controlled trial (CoAct)
ClinicalTrials.gov identifier: NCT00994565
The effectiveness of physical activity monitoring and distance counselling in an occupational health setting - a research protocol for a randomised controlled trial (CoAct)
ClinicalTrials.gov identifier: NCT00994565
Sin obras prominentes en esta página.
The effectiveness of physical activity monitoring and distance counselling in an occupational health setting - a research protocol for a randomised controlled trial (CoAct)
ClinicalTrials.gov identifier: NCT00994565
The effectiveness of physical activity monitoring and distance counseling in an occupational setting – Results from a randomized controlled trial (CoAct)
ClinicalTrials.gov identifier: NCT00994565
Understanding predictive information criteria for Bayesian models
Cost-effectiveness of providing patients with information on managing mild low-back symptoms in an occupational health setting
ClinicalTrials.gov NCT00908102
Practical Bayesian model evaluation using leave-one-out cross-validation and Waic
Effectiveness of three interventions for secondary prevention of low back pain in the occupational health setting - a randomised controlled trial with a natural course control
Number NCT00908102 Clinicaltrials.gov
R-squared for Bayesian Regression Models
The usual definition of R2 (variance of the predicted values divided by the variance of the data) has a problem for Bayesian fits, as the numerator can be larger than the denominator. We propose an alternative definition similar to one that has appeared in the survival analysis literature: the variance of the predicted values divided by the variance of predicted values plus the expected variance of the errors.
Visualization in Bayesian Workflow
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from …
Rank-Normalization, Folding, and Localization
Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic Rˆ of Gelman and Rubin (1992) has serious flaws. Traditional Rˆ will fail to correctly diagnose convergence failures when the chain has a heavy tail or when the variance varies across the chains. In this paper we propose an alte…
Bayesian probability (5 obras) · Medicine (4 obras) · Nursing (4 obras) · Physical therapy (4 obras) · Psychological intervention (4 obras) · Randomized controlled trial (4 obras) · Statistical Methods and Inference (4 obras) · Alternative medicine (3 obras) · Artificial Intelligence (3 obras) · Biostatistics (3 obras)