Strong rate of convergence of the Euler scheme for SDEs with irregular drift driven by Levy noise.
Oleg ButkovskyKonstantinos DareiotisMáté GerencsérPublished in: CoRR (2022)
Keyphrases
- general regression neural networks
- error accumulation
- arbitrary shape
- irregularly shaped
- image noise
- estimation error
- noise reduction
- signal to noise ratio
- convergence speed
- data driven
- number of iterations required
- arbitrarily shaped
- filtering method
- high level
- classification scheme
- noisy data
- noise level
- noise model
- high frequency
- random noise
- traffic load
- blocking probability
- missing data
- bandwidth utilization
- image restoration