An approach to outlier detection based on Bayesian probabilistic model

The problem of outlier detection is considered with reference to a piecewise-smooth signal corrupted by background Gaussian noise plus spikes. The problem of estimating the variance of background noise is considered and a robust algorithm which solves the problem in such an environment is suggested. The estimate of variance is essential for an outlier detection algorithm as well as for different algorithms for signal (image) analysis. Our approach to outlier detection is based on a Bayesian probabilistic model. The model enables selection of a set of informative tests for outlier detection. An experimental algorithm based on this approach is tested and its comparison with the median based approach is presented.