Jonah Gabry
Biographic Data
| ID | 10708406 |
|---|---|
| NAME | Jonah Gabry |
| GIVEN NAMES | Jonah |
| FAMILY NAME | Gabry |
| SIGNATURE | GABRY J |
| AFFILIATIONS | Columbia University , New York , USA |
| ORCID | 0000-0001-6437-6144 |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2019 |
| H-INDEX | 0 |
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.
Practical Bayesian model evaluation using leave-one-out cross-validation and Waic
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 …
No prominent works on this page.
Practical Bayesian model evaluation using leave-one-out cross-validation and Waic
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 …
Bayesian probability (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Data mining (2 works) · Data science (2 works) · Econometrics (2 works) · Mathematics (2 works) · Statistical Methods and Inference (2 works) · Analysis of variance (1 work) · Bayesian inference (1 work)