Synthetic Data Generation Capabilties for Testing Data Mining Tools

MILCOM 2006 ◽  
2006 ◽  
Author(s):  
Daniel Jeske ◽  
Pengyue Lin ◽  
Carlos Rendon ◽  
Rui Xiao ◽  
Behrokh Samadi
Keyword(s):  
Data Mining ◽  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Testing Data ◽  
Mining Tools

Synthetic data generation of high-resolution hyperspectral data using DIRSIG

2007 ◽  
Author(s):  
Marek K. Jakubowski ◽  
David Pogorzala ◽  
Timothy J. Hattenberger ◽  
Scott D. Brown ◽  
John R. Schott
Keyword(s):  
High Resolution ◽  
Synthetic Data ◽  
Hyperspectral Data ◽  
Data Generation ◽  
Synthetic Data Generation

Synthetic Data Generation to Support Irregular Sampling in Sensor Networks

2004 ◽  
pp. 211-234 ◽  
Author(s):  
Lewis Girod ◽  
Ramesh Govindan ◽  
Deepak Ganesan ◽  
Deborah Estrin ◽  
Yan Yu
Keyword(s):  
Sensor Networks ◽  
Synthetic Data ◽  
Irregular Sampling ◽  
Data Generation ◽  
Synthetic Data Generation

scholarly journals Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes

2021 ◽  
Author(s):  
Maria Lyssenko ◽  
Christoph Gladisch ◽  
Christian Heinzemann ◽  
Matthias Woehrle ◽  
Rudolph Triebel
Keyword(s):  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Crowded Scenes ◽  
Instance Segmentation

scholarly journals Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs

2019 ◽  
Vol 30 (3) ◽  
pp. 627-648 ◽  
Author(s):  
Evelyn Buckwar ◽  
Massimiliano Tamborrino ◽  
Irene Tubikanec
Keyword(s):  
Numerical Methods ◽  
Spectral Density ◽  
Diffusion Processes ◽  
Model Simulation ◽  
Broad Class ◽  
Synthetic Data ◽  
Summary Statistics ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Partially Observed

Abstract Approximate Bayesian computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed to an established tool for modelling time-dependent, real-world phenomena with underlying random effects. When applying ABC to stochastic models, two major difficulties arise: First, the derivation of effective summary statistics and proper distances is particularly challenging, since simulations from the stochastic process under the same parameter configuration result in different trajectories. Second, exact simulation schemes to generate trajectories from the stochastic model are rarely available, requiring the derivation of suitable numerical methods for the synthetic data generation. To obtain summaries that are less sensitive to the intrinsic stochasticity of the model, we propose to build up the statistical method (e.g. the choice of the summary statistics) on the underlying structural properties of the model. Here, we focus on the existence of an invariant measure and we map the data to their estimated invariant density and invariant spectral density. Then, to ensure that these model properties are kept in the synthetic data generation, we adopt measure-preserving numerical splitting schemes. The derived property-based and measure-preserving ABC method is illustrated on the broad class of partially observed Hamiltonian type SDEs, both with simulated data and with real electroencephalography data. The derived summaries are particularly robust to the model simulation, and this fact, combined with the proposed reliable numerical scheme, yields accurate ABC inference. In contrast, the inference returned using standard numerical methods (Euler–Maruyama discretisation) fails. The proposed ingredients can be incorporated into any type of ABC algorithm and directly applied to all SDEs that are characterised by an invariant distribution and for which a measure-preserving numerical method can be derived.

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