Abstract A new, analytical approach using the cumulant method is proposed for three-phase probabilistic power-flow (PPF) analysis. The to forming sensitivity matrix based on quantifying pseudo-inverse instead of inverse jacobian matrix, since it commonly singular in a distribution power network. results are compared with those obtained point-estimate (PEM) and Monte Carlo (MC) method, which use...

Gaussian processes (GPs) are flexible distributions over functions that enable highlevel assumptions about unknown functions to be encoded in a parsimonious, flexible and general way. Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise when data are sufficiently numerous or when employing non-Gaussian models. Consequently, a wealth of ...

We develop a language model using probabilistic context-free grammars (PCFGs) that is \pseudo context-sensitive" in that the probability that a non-terminal N expands using a rule r depends on N's parent. We derive the equations for estimating the necessary probabilities using a variant of the inside-outside algorithm. We give experimental results showing that, beginning with a high-performance...

In the TREC 2005 robust retrieval track, we tested our adaptive retrieval model that automatically switches between the 2-Poisson model/adaptive vector space model and our initial predictive probabilistic contextbased model depending on some query characteristics. Our 2-Poisson model uses the BM11 term weighting scheme with passage retrieval and pseudo-relevance feedback. The context-based mode...

In this paper, we propose a novel discriminative language model, which can be applied quite generally. Compared to the well known N-gram language models, discriminative language models can achieve more accurate discrimination because they can employ overlapping features and nonlocal information. However, discriminative language models have been used only for re-ranking in specific applications ...

Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the hyperparameter estimates are updated in an online ...

In this article, we construct a random number generator using a one dimensional, nonuniform 4-Neighborhood Cellular Automata (4NCA). A probabilistic analysis of CA rules has been done to select the appropriate rules for 4NCA random number generator (RNG). A comparison is made between the pseudo random patterns generated by using the proposed CA RNGs with those obtained using a recent Cellular P...

We develop a language model using probabilistic context-free grammars (PCFGs) that is “pseudo context-sensitive” in that the probability that a nonterminal N expands using a rule T depends on N’s parent. We give the equations for estimating the necessary probabilities using a variant of the inside-outside algorithm. We give experimental results showing that, beginning with a high-performance PC...

Zero-resource speech processing involves the automatic analysis of a collection of speech data in a completely unsupervised fashion without the benefit of any transcriptions or annotations of the data. In this thesis, we describe a zero-resource framework that automatically discovers important words, phrases and topical themes present in an audio corpus. This system employs a segmental dynamic ...