481 research outputs found
On the sequential massart algorithm for statistical model checking
Several schemes have been provided in Statistical Model Checking (SMC) for the estimation of property occurrence based on predefined confidence and absolute or relative error. Simulations might be however costly if many samples are required and the usual algorithms implemented in statistical model checkers tend to be conservative. Bayesian and rare event techniques can be used to reduce the sample size but they can not be applied without prerequisite or knowledge about the system under scrutiny. Recently, sequential algorithms based on Monte Carlo estimations and Massart bounds have been proposed to reduce the sample size while providing guarantees on error bounds which has been shown to outperform alternative frequentist approaches [15]. In this work, we discuss some features regarding the distribution and the optimisation of these algorithms.No Full Tex
Sequential schemes for frequentist estimation of properties in statistical model checking
National Research Foundation (NRF) Singapor
Statistical Guarantees for the Robustness of Bayesian Neural Networks
We introduce a probabilistic robustness measure for Bayesian Neural Networks
(BNNs), defined as the probability that, given a test point, there exists a
point within a bounded set such that the BNN prediction differs between the
two. Such a measure can be used, for instance, to quantify the probability of
the existence of adversarial examples. Building on statistical verification
techniques for probabilistic models, we develop a framework that allows us to
estimate probabilistic robustness for a BNN with statistical guarantees, i.e.,
with a priori error and confidence bounds. We provide experimental comparison
for several approximate BNN inference techniques on image classification tasks
associated to MNIST and a two-class subset of the GTSRB dataset. Our results
enable quantification of uncertainty of BNN predictions in adversarial
settings.Comment: 9 pages, 6 figure
Joint segmentation of wind speed and direction using a hierarchical model
The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series. Segmentation is then conducted using a hierarchical Bayesian model that accounts for correlations between the wind speed and direction. A Gibbs sampling strategy overcomes the computational complexity of the hierarchical model and is used to estimate the unknown parameters and hyperparameters. Extensions to other statistical models are also discussed. These models allow us to study other joint segmentation problems including segmentation of wave amplitude and direction. The performance of the proposed algorithms is illustrated with results obtained with synthetic and real data
About adaptive coding on countable alphabets
This paper sheds light on universal coding with respect to classes of
memoryless sources over a countable alphabet defined by an envelope function
with finite and non-decreasing hazard rate. We prove that the auto-censuring AC
code introduced by Bontemps (2011) is adaptive with respect to the collection
of such classes. The analysis builds on the tight characterization of universal
redundancy rate in terms of metric entropy % of small source classes by Opper
and Haussler (1997) and on a careful analysis of the performance of the
AC-coding algorithm. The latter relies on non-asymptotic bounds for maxima of
samples from discrete distributions with finite and non-decreasing hazard rate
The Classification of Workforce Requirement Planning for Service Oriented Operations
In today’s world of competitive international economy sectors, service industry orservice sector oriented businesses, the key point is to maximize the efficiency and sustainability of the business directly related with optimal planning of the workload and distributing them among the employees. Helpdesks and operation centers are one of the fastest developing service area of this sector. This paper compares the machine learning algorithms that can be used for the classification of workforce requirements for a bank operation center which provides support to reduce operational workload of bank branches. Classification of the workload based on the quantity of Money Order and EFT operations within time zones aids in the management of workforce teams and distribution of jobs between team members
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