The classical method of process capability analysis necessarily assumes that collected data are independent; nonetheless, some processes such as biological and chemical processes are autocorrelated and violate the independency assumption. Many processes exhibit a certain degree of correlation and can be treated by autoregressive models among which the autoregressive model of order one (AR (1)) ...

This paper investigates the applicability of high order statistical autoregressive (AR-HOS) modeling method in analyzing biomedical signals. The autoregressive (AR) method using linear prediction and AR-HOS method using cumulants are applied on normal and pathological heart sound signals. It is found that the AR-HOS modeling a signal produce more accurate and higher resolution spectrum than AR ...

Observations on the Cure and Prevention of the contagious Fever nozo prevalent in the City of Edinburgh and its Environs, fyc. cfc. By J. Yule, M. D. F. R. S. Edin. Edinburgh, 1818. pp. 58. This pamphlet, as the title expresses, is confined almost entirely to prophylaxis, including etiology and treatment. In respect to the origin of the fever, Dr. Yule advocates its domestic source, especially ...

This paper presents two results on sample paths for the Yule process: one fluid limit theorem and one sample path large deviation result. The main interest is to understand the way large deviation occurs in the case of non-homogeneous processes. There are indeed two new phenomena. First there is no “typical” speed of large deviation. Second, the large deviation event is concentrated on a finite...

Seismic tomography used in the laboratory, as well as in the field, is strongly affected by limited and nonuniform ray coverage. A two-stage autoregressive extrapolation technique is proposed that can be used to extend the observed data and provide better tomographic images. The algorithm is based on the principle that the extrapolated data add minimal information to the existing data. The firs...

We propose a simple and practical method for improving the flexibility of the approximate posterior in variational auto-encoders (VAEs) through a transformation with autoregressive networks. Autoregressive networks, such as RNNs and RNADE networks, are very powerful models. However, their sequential nature makes them impractical for direct use with VAEs, as sequentially sampling the latent vari...