Abstract: In this paper, we present an overdispersed count data clustering algorithm, which uses the mesh method for computing the log-likelihood function, of the multinomial Beta-Liouville ...
The need to combine likelihood information is common in analyses of complex models and in meta-analyses, where information is combined from several studies. We work to first order, and show that full ...
Abstract: In CRYPTO 2013, Ducas et al. introduced a bimodal discrete Gaussian distribution into the Fiat-Shamir with abort paradigm, proposing a signature scheme called BLISS, which significantly ...
The last time I went to the doctor, I lived in a different zip code, belonged to a different body-weight category, and was rounding out a different decade. I never consciously swore off health care, ...
ABSTRACT: Count data is almost always over-dispersed where the variance exceeds the mean. Several count data models have been proposed by researchers but the problem of over-dispersion still remains ...
Gen Z cat lovers don't just believe in the cat distribution system − they spread the message via memes and T-shirts. Stories of the so-called cat distribution system seem to propel the phenomenon ...
The focus of this article is on the nature of the likelihood associated with N-mixture models for repeated count data. It is shown that the infinite sum embedded in the likelihood associated with the ...
The binomial probability is a widely-used concept in statistics, helping to answer questions about the likelihood of certain outcomes in an experiment or real-life situation. Essentially, it measures ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback