Corpus ID: 10394967

Journal of the Royal Statistical Society Notes on the Submission of Papers

  title={Journal of the Royal Statistical Society Notes on the Submission of Papers},
  author={Arthur P. Dempster and Roland Askew and R. R. Burridge and William Bluethmann and Myron A. Diftler and Chris Lovchik and Daniel Summer Magruder and Frederik Rehnmark and Frank E. Pollick},
Some journals have policies requiring authors of submitted papers to declare potential conflicts of interest. The purpose is not to remove the conflict but to publicize it, and to allow readers to form their own conclusions on whether any conflict of interest exists. For many of the papers submitted to the Journal of the Royal Statistical Society this is unlikely to be an issue. However, such interests may take many forms, including financial considerations and situations where one or more of… 


Inference from Iterative Simulation Using Multiple Sequences
The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative
What Is a Knowledge Representation?
It is argued that keeping in mind all five of these roles that a representation plays provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.
Reasoning about uncertainty
This second edition has been updated to reflect Halpern's recent research and includes a consideration of weighted probability measures and how they can be used in decision making.
Fuzzy models for pattern recognition
The basic structure of fuzzy sets theory as it applies to the major problems encountered in the design of a pattern recognition system is described.
Detection of Outbreaks from Time Series Data Using Wavelet Transform
In this paper, we developed a new approach to detection of disease outbreaks based on wavelet transform. It is capable of dealing with two problems found in real-world time series data, namely,
Machine learning
Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
A possibilistic approach to clustering
An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Training HMM structure with genetic algorithm for biological sequence analysis
The proposed GA for hidden Markov models (GA-HMM) allows, HMMs with different numbers of states to evolve, and it was capable of finding an HMM comparable to a hand-coded HMM designed for the same task, which has been published previously.
Competitive fuzzy clustering
In this paper, we introduce a new approach called Competitive Agglomeration (CA), which combines the advantages of hierarchical and partitional clustering techniques. The CA algorithm starts by
The possibilistic C-means algorithm: insights and recommendations
The underlying principles of the PCM and the possibilistic approach, in general are examined and the results reported by Barni et al. are interpreted in the light of their findings.