4 citations · 5 across the 4 of their papers we have counts for
5 papers
Bayesian adaptive design for neural recruitment curves
Vishweshwar Tyagi, Saihari S. Dukkipati, Daniel Twili +4
Nonlinear response models remain ubiquitous across disciplines with recruitment curves describing muscle response to neural stimulation. Conventional acquisition methods typically…
The Squealer: Sensification of model exploration and model misfit
Andrew Gelman, Andrew H. Jaffe, Eliot Carlson +1
We introduce a method for visual and auditory feedback when exploring the fit of a model to data. Starting with a best-fit curve fit to data, the user can drag the curve to a new p…
Approximate posterior recalibration
Tiffany Cai, Philip Greengard, Ben Goodrich +1
Bayesian inference is often implemented using approximations, which can yield interval estimates that are too narrow, not fully capturing the uncertainty in the posterior distribut…
Russian roulette: The need for stochastic potential outcomes when utilities depend on counterfactuals
Andrew Gelman, Jonas M. Mikhaeil
It has been proposed in medical decision analysis to express the ``first do no harm'' principle as an asymmetric utility function in which the loss from killing a patient would cou…
Hierarchical Bayesian Models to Mitigate Systematic Disparities in Prediction with Proxy Outcomes
Jonas Mikhaeil, Andrew Gelman, Philip Greengard
Label bias occurs when the outcome of interest is not directly observable and instead, modeling is performed with proxy labels. When the difference between the true outcome and the…