tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure

9 September 2020

Papers citing "tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure"

5 / 5 papers shown

Title
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach M. Tadayon G. Pottie BDL AI4TS 24 5 0 09 Aug 2020
Predicting Student Performance in an Educational Game Using a Hidden Markov Model M. Tadayon Gregory Pottie 18 34 0 24 Apr 2019
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks Christopher Bowles Liang Chen Ricardo Guerrero P. Bentley R. Gunn A. Hammers D. A. Dickie M. V. Valdés Hernández Joanna M. Wardlaw Daniel Rueckert GAN MedIm 57 424 0 25 Oct 2018
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification Maayan Frid-Adar Eyal Klang Michal Amitai Jacob Goldberger H. Greenspan MedIm GAN 49 697 0 08 Jan 2018
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs Cristóbal Esteban Stephanie L. Hyland Gunnar Rätsch GAN SyDa MedIm 85 781 0 08 Jun 2017