no code implementations • 1 Nov 2021 • Supriya Nagesh, Alexander Moreno, Stephanie M. Carpenter, Jamie Yap, Soujanya Chatterjee, Steven Lloyd Lizotte, Neng Wan, Santosh Kumar, Cho Lam, David W. Wetter, Inbal Nahum-Shani, James M. Rehg
The transformer model achieves a non-response prediction AUC of 0. 77 and is significantly better than classical ML and LSTM-based deep learning models.
Theoretically, we show new existence results for both kernel exponential and deformed exponential families, and that the deformed case has similar approximation capabilities to kernel exponential families.
We solve the first challenge by reformulating the estimation problem as an equivalent discrete time-inhomogeneous hidden Markov model.
Panel count data describes aggregated counts of recurrent events observed at discrete time points.
We present a parameter learning method for GLM emissions and survival model fitting, and present promising results on both synthetic data and an mHealth drug use dataset.
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models.