Abelian group, 278 acceptance probability, 24, 37, 246 adaptive Metropolis algorithm, 33 Akaike’s information criterion, 209, 320 alpha-beta recursion, 11, 390 annealed importance sampling, 321 aperiodic chain, 23 APF, see auxiliary particle filter AR model, see autoregressive model assumed density filtering, 17, 143, 388 autoregressive hidden Markov model, 185 autoregressive model, 4, 9, 111 a...

We analyze the asymptotic behavior of autoregressive neural network (AR-NN) processes using techniques from Markov chains and non-linear time series analysis. It is shown that standard AR-NNs without shortcut connections are asymptotically stationary. If linear shortcut connections are allowed, only the shortcut weights determine whether the overall system is stationary, hence standard conditio...

Several investigators of EEG time series reported a rejection of the null hypothesis of linear stochastic dynamics for epochs longer than 10 s. We examine whether this rejection is related to nonlinearity or to nonstationarity. Our approach is a combination of autoregressive modeling and surrogate data testing. It is shown that the fraction of subsegments, for which the null hypothesis has to b...

Chaotic signals generated by iterating nonlinear difference equations may be useful models for many natural phenomena. In this paper we propose a family of chaotic models for signal processing applications. The chaotic signals generated by this family of first order difference equations have autocorrelations identical to stochastic first-order autoregressive (AR) processes. After considering th...

Hands-free speech input is required in many modern telecommunication applications that employ autoregressive (AR) techniques such as linear predictive coding. When the hands-free input is obtained in enclosed reverberant spaces such as typical office rooms, the speech signal is distorted by the room transfer function. This paper utilizes theoretical results from statistical room acoustics to an...

The autoregressive (AR) model is widely used in modeling image, speech and EEG signals. Using this model as the model for the host signal, we have devised a watermarking algorithm which is compliant with the power spectrum condition. This is achieved by embedding the quantization watermark in the residual signal of the AR model, both for dither modulation (DM) watermarking and spread-transform ...

Most of the signals recorded in experiments are inevitably contaminated by measurement noise. Hence, it is important to understand the effect of such noise on estimating causal relations between such signals. A primary tool for estimating causality is Granger causality. Granger causality can be computed by modeling the signal using a bivariate autoregressive (AR) process. In this paper, we grea...

In this paper, we propose a robust delay estimation scheme for Internet multimedia applications. The proposed scheme adopts an autoregressive (AR) model for the delay process. The adopted AR model is a natural generalization of the Jacobson’s algorithm in TCP retransmission timeout (RTO) estimation. However, unlike fixed parameter values in the Jacobson’s algorithm, the parameters of the propos...

The least squares (LS) estimation criterion on one hand, and the total LS (TLS), constrained TLS (CTLS) and structured TLS (STLS) criteria on the other hand, can be viewed as opposite limiting cases of a more general criterion, which we term “Extended LS” (XLS). The XLS criterion distinguishes measurement errors from modeling errors by properly weighting and balancing the two error sources. In ...