Topic
Three-point estimation
About: Three-point estimation is a research topic. Over the lifetime, 378 publications have been published within this topic receiving 11847 citations.
Papers published on a yearly basis
1 / 2
Papers
TL;DR: The EM (expectation-maximization) algorithm is ideally suited to problems of parameter estimation, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation.
Abstract: A common task in signal processing is the estimation of the parameters of a probability distribution function Perhaps the most frequently encountered estimation problem is the estimation of the mean of a signal in noise In many parameter estimation problems the situation is more complicated because direct access to the data necessary to estimate the parameters is impossible, or some of the data are missing Such difficulties arise when an outcome is a result of an accumulation of simpler outcomes, or when outcomes are clumped together, for example, in a binning or histogram operation There may also be data dropouts or clustering in such a way that the number of underlying data points is unknown (censoring and/or truncation) The EM (expectation-maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation The EM algorithm is presented at a level suitable for signal processing practitioners who have had some exposure to estimation theory
3,166 citations
TL;DR: In this paper, a measure of the information provided by an experiment is introduced, derived from the work of Shannon and involves the knowledge prior to performing the experiment, expressed through a prior probability distribution over the parameter space.
Abstract: A measure is introduced of the information provided by an experiment. The measure is derived from the work of Shannon [10] and involves the knowledge prior to performing the experiment, expressed through a prior probability distribution over the parameter space. The measure is used to compare some pairs of experiments without reference to prior distributions; this method of comparison is contrasted with the methods discussed by Blackwell. Finally, the measure is applied to provide a solution to some problems of experimental design, where the object of experimentation is not to reach decisions but rather to gain knowledge about the world.
1,708 citations
TL;DR: In this article, the long-term fluctuation of transmission loss in scatter propagation systems has been found to have a logarithmic-normal distribution, i.e., the scatter loss in decibels has Gaussian statistical distribution.
Abstract: The long-term fluctuation of transmission loss in scatter propagation systems has been found to have a logarithmicnormal distribution. In other words, the scatter loss in decibels has Gaussian statistical distribution. Therefore, in many important communication systems (e.g., FM), the noise power of a radio jump, or hop, has log-normal statistical distribution. In a multihop system, the noise power of each hop contributes to the total noise. The resulting noise of the system is therefore the statistical sum of the individual noise distributions. In multihop scatter systems and others, such as multichannel speech-transmission systems, the sum of several log-normal distributions is needed. No exact solution to this problem is known. The following discussion presents an approximate solution which is satisfactory in most practical cases. For tactical multihop scatter systems, a further approximation is proposed, which reduces significantly the necessary computation. An example of the computation is given.
902 citations
TL;DR: New properties of this transformation of a probability distribution into a possibility distribution are described, by relating it with the well-known probability inequalities of Bienaymé-Chebychev and Camp-Meidel.
Abstract: A possibility measure can encode a family of probability measures. This fact is the basis for a transformation of a probability distribution into a possibility distribution that generalises the notion of best interval substitute to a probability distribution with prescribed confidence. This paper describes new properties of this transformation, by relating it with the well-known probability inequalities of Bienayme-Chebychev and Camp-Meidel. The paper also provides a justification of symmetric triangular fuzzy numbers in the spirit of such inequalities. It shows that the cuts of such a triangular fuzzy number contains the “confidence intervals” of any symmetric probability distribution with the same mode and support. This result is also the basis of a fuzzy approach to the representation of uncertainty in measurement. It consists in representing measurements by a family of nested intervals with various confidence levels. From the operational point of view, the proposed representation is compatible with the recommendations of the ISO Guide for the expression of uncertainty in physical measurement.
594 citations
TL;DR: A three-phase distribution system state estimation algorithm is proposed in this paper, where the normal equation method is used to compute the real-time states of distribution systems modeled by their actual a-b-c phases.
Abstract: A three-phase distribution system state estimation algorithm is proposed in this paper. The normal equation method is used to compute the real-time states of distribution systems modeled by their actual a-b-c phases. A current based formulation is introduced and compared with other formulations. Observability analysis for the proposed distribution system state estimation is discussed. Test results indicate that the normal equation method is applicable to the distribution system state estimation and the current based rectangular form formulation is suitable for this application. >
497 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 2 |
| 2019 | 1 |
| 2018 | 2 |
| 2017 | 11 |
| 2016 | 23 |
| 2015 | 15 |