In this section
@article{Tindemans:2017:10.1098/rsta.2016.0299,
author = {Tindemans, S and Strbac, G},
doi = {10.1098/rsta.2016.0299},
journal = {Philosophical Transactions A: Mathematical, Physical and Engineering Sciences},
title = {Robust estimation of risks from small samples},
url = {http://dx.doi.org/10.1098/rsta.2016.0299},
volume = {375},
year = {2017}
}
TY - JOUR
AB - Data-driven risk analysis involves the inference of probability distributions from measured or simulated data. In the case of a highly reliable system, such as the electricity grid, the amount of relevant data is often exceedingly limited, but the impact of estimation errors may be very large. This paper presents a robust non-parametric Bayesian method to infer possible underlying distributions. The method obtains rigorous error bounds even for small samples taken from ill-behaved distributions. The approach taken has a natural interpretation in terms of the intervals between ordered observations, where allocation of probability mass across intervals is well specified, but the location of that mass within each interval is unconstrained. This formulation gives rise to a straightforward computational resampling method: Bayesian interval sampling. In a comparison with common alternative approaches, it is shown to satisfy strict error bounds even for ill-behaved distributions.
AU - Tindemans,S
AU - Strbac,G
DO - 10.1098/rsta.2016.0299
PY - 2017///
SN - 1471-2962
TI - Robust estimation of risks from small samples
T2 - Philosophical Transactions A: Mathematical, Physical and Engineering Sciences
UR - http://dx.doi.org/10.1098/rsta.2016.0299
UR - http://arxiv.org/abs/1311.5052v3
UR - http://hdl.handle.net/10044/1/46104
VL - 375
ER -