Two new algorithms are presented for the segmentation of a white Gaussian-distributed time series having unknown but piecewise-constant variances. The first “sequential/minimum description length (MDL)” idea includes a rough parsing via the GLR, a penalization of segmentations having too many parts via MDL, and an optional refinement stage. The second “Gibbs sampling” approach is Bayesian and d...

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. The...

In this paper we propose a batch learning algorithm for sequential blind extraction of arbitrary distributed but generally not i.i.d. (independent identically distributed) temporally correlated sources, possibly dependent speech signals from from linear mixture of them. The proposed algorithm is computationally very simple and eÆcient, it is based only on the second order statistics and in cont...

this paper studies the sequential sampling scheme, as a solution to the problem of aliasing, where the sampling interval is restricted to a minimum allowable value d t. in the sequential sampling, the signal is sampled at intervals of d t, d t+ dt , d t+2 dt , d t+3 dt , ...; where dt < d t and may be selected as desirable. the sequential sampling is, however, analyzed and it is proven that, wh...

We consider Gaussian quantum circuits supplemented with non-Gaussian input states and derive sufficient conditions for efficient classical strong simulation of these circuits. In particular, we generalise the stellar representation continuous-variable to multimode setting relate rank states, a recently introduced measure non-Gaussianity, cost evaluating classically output probability densities ...

This paper presents a novel adaptive neuro-fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part. The capability of the proposed ANFIS2 for function approximation and dynamical system identification is remarkable. The structure of ANFIS2 is very sim...

An adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented in this paper. The capability of the proposed method (we named ANFIS2) for function approximation and dynamical system identification is remarkable. The structure o...