The author introduced the “geometric AIC” and the “geometric MDL” as model selection criteria for geometric fitting problems. These correspond to Akaike’s “AIC” and Rissanen’s “BIC”, respectively, well known in the statistical estimation framework. Another criterion well known is Schwarz’ “BIC”, but its counterpart for geometric fitting has been unknown. This paper introduces the corresponding ...

In this paper I present a Minimum Description Length Estimator for number of sources in an anechoic mixture of sparse signals. The criterion is roughly equal to the sum of negative normalized maximum log-likelihood and the logarithm of number of sources. Numerical evidence supports this approach and compares favorabily to both the Akaike (AIC) and Bayesian (BIC) Information Criteria. 1 Signal a...

BACKGROUND AND OBJECTIVES: The classification of marine animals as protected species makes data and information on them to be very important. Therefore, this led to the need to retrieve and understand the data on the event counts for stranded marine animals based on location emergence, number of individuals, behavior, and threats to their presence. Whales are g...

We discuss how to characterize entanglement sources with finite sets of measurements. The measurements do not have to be tomographically complete and may consist of POVMs rather than von Neumann measurements. Our method yields a probability that the source generates an entangled state as well as estimates of any desired calculable entanglement measures, including their error bars. We apply two ...

In recent years, artificial neural networks have been used for time series forecasting. Determining architecture of artificial neural networks is very important problem in the applications. In this study, the problem in which time series are forecasted by feed forward neural networks is examined. Various model selection criteria have been used for the determining architecture. In addition, a ne...

This study investigated speaker variation in the production of various acoustic cues of prominence, including duration and intensity measures. The Bayesian Information Criterion was used to specify a threshold distinction between cues that are linearly vs. piece-wise linearly predictors of the degree of perceived prominence. For all speakers, some features are linear and some features are discr...

A problem in model selection, namely the identification of multiple change points for a piece-wise constant hazard rate, is discussed. A methodology using the Bayes’ Information Criterion is developed in an overdispersed survival model (with corresponding quasi-likelihood function). The technique is used to identify changes in the historical frequency of forest fire. It is applied to two datase...

Statistical methods for voice conversion are usually based on a single model selected in order to represent a tradeoff between goodness of fit and complexity. In this paper we assume that the best model may change over time, depending on the source acoustic features. We present a new method for spectral voice conversion called Dynamic Model Selection (DMS), in which a set of potential best mode...

The Principal Direction Divisive Partitioning (PDDP) algorithm is a fast and scalable clustering algorithm [3]. The basic idea is to recursively split the data set into sub-clusters based on principal direction vectors. However, the PDDP algorithm can yield poor results, especially when cluster structures are not well-separated from one another. Its stopping criterion is based on a heuristic th...