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Mixtures of Unimodal Distributions
"... Carlos E. Rodríguez1,2,⋆, Stephen G. Walker1 We present a new Bayesian mixture model. The main idea of our proposal is to change the components distribution of the mixture. Whereas the normal distribution is typically used as the kernel distribution, it does have some serious issues for the modeling ..."
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the only constraint is that they are unimodal. Hence, we define a cluster as a set of data which can be adequately modeled via a unimodal distribution. To construct unimodal distributions we use a mixture of unform distributions with the Dirichlet Process, (Ferguson (1973) and Sethuraman (1994
On Homogeneous Skewness of Unimodal Distributions
"... We introduce a new concept of skewness for unimodal continuous distributions which is built on the asymmetry of the density function around its mode. The asymmetry is captured through a skewness function. We call a distribution homogeneously skewed if this skewness function is consistently positive ..."
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We introduce a new concept of skewness for unimodal continuous distributions which is built on the asymmetry of the density function around its mode. The asymmetry is captured through a skewness function. We call a distribution homogeneously skewed if this skewness function is consistently positive
Tailweight with respect to the mode for unimodal distributions
, 1993
"... Location, spread, skewness and tailweight are studied for unimodal distributions by means of modebased concepts. The Lrvy concentration function and notions related to it are playing an important part. AMS Subject Classification: Primary 62E10; Secondary 60E99 ..."
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Location, spread, skewness and tailweight are studied for unimodal distributions by means of modebased concepts. The Lrvy concentration function and notions related to it are playing an important part. AMS Subject Classification: Primary 62E10; Secondary 60E99
On the elicitation of continuous, symmetric, unimodal distributions
, 2008
"... In this brief note, we highlight some difficulties that can arise when fitting a continuous, symmetric, unimodal distribution to a set of expert’s judgements. A simple analysis shows it is possible to fit a Cauchy distribution to an expert’s beliefs when their beliefs actually follow a normal distri ..."
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In this brief note, we highlight some difficulties that can arise when fitting a continuous, symmetric, unimodal distribution to a set of expert’s judgements. A simple analysis shows it is possible to fit a Cauchy distribution to an expert’s beliefs when their beliefs actually follow a normal
Sublinear algorithms for testing monotone and unimodal distributions
 Proceedings of STOC 36th
, 2004
"... The complexity of testing properties of monotone and unimodal distributions, when given access only to samples of the distribution, is investigated. Two kinds of sublineartime algorithms—those for testing monotonicity and those that take advantage of monotonicity—are provided. The first algorithm te ..."
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Cited by 30 (10 self)
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The complexity of testing properties of monotone and unimodal distributions, when given access only to samples of the distribution, is investigated. Two kinds of sublineartime algorithms—those for testing monotonicity and those that take advantage of monotonicity—are provided. The first algorithm
ON MONOTONIC BEHAVIOUR OF RELATIVE INCREMENTS OF UNIMODAL DISTRIBUTIONS
"... Abstract. Sufficient conditions for monotonic behaviour of relative increment and hazard rate functions h of unimodal distributions of types U and J are being investigated, proved and then applied to some distributions. In addition, a general algorithm for checking monotonic properties of h is give ..."
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Abstract. Sufficient conditions for monotonic behaviour of relative increment and hazard rate functions h of unimodal distributions of types U and J are being investigated, proved and then applied to some distributions. In addition, a general algorithm for checking monotonic properties of h
Sublinear Algorithms for Testing Monotone and Unimodal Distributions ABSTRACT
"... The complexity of testing properties of monotone and unimodal distributions, when given access only to samples of the distribution, is investigated. Two kinds of sublineartime algorithms—those for testing monotonicity and those that take advantage of monotonicity—are provided. The first algorithm te ..."
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The complexity of testing properties of monotone and unimodal distributions, when given access only to samples of the distribution, is investigated. Two kinds of sublineartime algorithms—those for testing monotonicity and those that take advantage of monotonicity—are provided. The first algorithm
The Patchwork Rejection Technique for Sampling from Unimodal Distributions
, 1998
"... We report on both theoretical developments of and computational experience with the patchwork rejection technique as studied in [20] and [21]. The basic approach is due to Minh [13] who suggested a special sampling method for the gamma distribution. Its general objective is to rearrange the area bel ..."
Squared Skewness Minus Kurtosis Bounded By 186/125 for Unimodal Distributions
"... The sharp inequality for squared skewness minus kurtosis is derived for the class of unimodal distributions. Keywords: skewness, kurtosis, unimodal distributions, innitely divisible distributions. 1 Notation, the inequality and its history Let F be a nondegenerate distribution with nite fourth mome ..."
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Cited by 3 (1 self)
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The sharp inequality for squared skewness minus kurtosis is derived for the class of unimodal distributions. Keywords: skewness, kurtosis, unimodal distributions, innitely divisible distributions. 1 Notation, the inequality and its history Let F be a nondegenerate distribution with nite fourth
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
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Cited by 705 (15 self)
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We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture
Results 1  10
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40,641