A classification of the main probability distributions by minimizing the weighted logarithmic measure of deviation

1990 ◽  
Vol 42 (2) ◽  
pp. 269-279 ◽  
Author(s):  
Silviu Guiasu
Keyword(s):  
Probability Distributions ◽  
Logarithmic Measure

scholarly journals Learn from every mistake! Hierarchical information combination in astronomy

2016 ◽  
Vol 12 (S325) ◽  
pp. 39-45 ◽  
Author(s):  
Maria Süveges ◽  
Sotiria Fotopoulou ◽  
Jean Coupon ◽  
Stéphane Paltani ◽  
Laurent Eyer ◽  
...  
Keyword(s):  
Big Data ◽  
Probability Distributions ◽  
Variable Stars ◽  
Alternative Methods ◽  
Photometric Redshift ◽  
Promising Tool ◽  
Combination Methods ◽  
General Improvement ◽  
Output Probability

AbstractThroughout the processing and analysis of survey data, a ubiquitous issue nowadays is that we are spoilt for choice when we need to select a methodology for some of its steps. The alternative methods usually fail and excel in different data regions, and have various advantages and drawbacks, so a combination that unites the strengths of all while suppressing the weaknesses is desirable. We propose to use a two-level hierarchy of learners. Its first level consists of training and applying the possible base methods on the first part of a known set. At the second level, we feed the output probability distributions from all base methods to a second learner trained on the remaining known objects. Using classification of variable stars and photometric redshift estimation as examples, we show that the hierarchical combination is capable of achieving general improvement over averaging-type combination methods, correcting systematics present in all base methods, is easy to train and apply, and thus, it is a promising tool in the astronomical “Big Data” era.

Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

2021 ◽  
Vol 163 (A3) ◽  
Author(s):  
B Shabani ◽  
J Ali-Lavroff ◽  
D S Holloway ◽  
S Penev ◽  
D Dessi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Probability Distributions ◽  
Feature Space ◽  
Support Vector ◽  
Acceleration Threshold ◽  
Vector Machines ◽  
Structural Responses ◽  
As Stress

An onboard monitoring system can measure features such as stress cycles counts and provide warnings due to slamming. Considering current technology trends there is the opportunity of incorporating machine learning methods into monitoring systems. A hull monitoring system has been developed and installed on a 111 m wave piercing catamaran (Hull 091) to remotely monitor the ship kinematics and hull structural responses. Parallel to that, an existing dataset of a similar vessel (Hull 061) was analysed using unsupervised and supervised learning models; these were found to be beneficial for the classification of bow entry events according to key kinematic parameters. A comparison of different algorithms including linear support vector machines, naïve Bayes and decision tree for the bow entry classification were conducted. In addition, using empirical probability distributions, the likelihood of wet-deck slamming was estimated given a vertical bow acceleration threshold of 1  in head seas, clustering the feature space with the approximate probabilities of 0.001, 0.030 and 0.25.

MODELLING A COST PROFILE FOR ROAD PROJECTS

2020 ◽  
Author(s):  
Opeoluwa Akinradewo ◽  
Clinton Aigbavboa ◽  
Ayodeji Emmanuel Oke ◽  
Harrison Coffie
Keyword(s):  
Probability Distributions ◽  
Descriptive Analysis ◽  
Construction Cost ◽  
Cost Data ◽  
Density Functions ◽  
Cost Overrun ◽  
Historical Cost ◽  
Road Projects ◽  
Pro Forma

One of the vital success elements of a construction project is the accuracy of the estimation of construction cost. This study is aimed at developing a cost profile for road projects in Ghana. Pro-forma was designed to retrieve historical cost data of completed road projects in Ghana. The pro-forma retrieved data such as the initial budgeted cost and final construction cost of road projects, location of road projects, features of road projects, the scope of road projects (New project, renovation work, upgrade work or replacement work), type of road projects and classification of road projects. Cost data were analysed using descriptive analysis and probability distributions such as Cumulative Density Functions and Probability Density Functions. From findings, estimates prepared for road projects in Ghana can be expected to be below the final construction cost by about 20% while most of the completed road projects in Ghana experience cost overrun.

Probabilistic Preferences Composition in the Classification of Apparel Retail Stores

2015 ◽  
Vol 2 (4) ◽  
pp. 64-78 ◽  
Author(s):  
Rodrigo Otávio de Araújo Ribeiro ◽  
Lidia Angulo Meza ◽  
Annibal Parracho Sant'Anna
Keyword(s):  
Probability Distributions ◽  
Multicriteria Analysis ◽  
Operational Efficiency ◽  
Retail Stores ◽  
Analysis Methodology ◽  
Location Parameters ◽  
Multiple Attributes

This paper employs the probabilistic composition of preferences to classify stores by their operational efficiency. Probabilistic composition of preferences is a multicriteria analysis methodology based on the transformation of assessments by multiple attributes into probabilities of choice. The numerical initial measurements provide estimates for location parameters of probability distributions that are compared to measure the preferences. The probabilities of choice according to each attribute separately are aggregated according to probabilistic composition rules. A classification of two sets of stores into five classes is performed.

scholarly journals Quantification of Scenario Distance within Generic WINNER Channel Model

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Milan Narandžić ◽  
Christian Schneider ◽  
Wim Kotterman ◽  
Reiner S. Thomä
Keyword(s):  
Channel Model ◽  
Probability Distributions ◽  
Correlation Coefficients ◽  
Mimo Channel ◽  
Normal Probability ◽  
Leibler Divergence ◽  
Suitable Space ◽  
Indoor Scenarios ◽  
Stochastic Properties

Starting from the premise that stochastic properties of a radio environment can be abstracted by defining scenarios, a generic MIMO channel model is built by the WINNER project. The parameter space of the WINNER model is, among others, described by normal probability distributions and correlation coefficients that provide a suitable space for scenario comparison. The possibility to quantify the distance between reference scenarios and measurements enables objective comparison and classification of measurements into scenario classes. In this paper we approximate the WINNER scenarios with multivariate normal distributions and then use the mean Kullback-Leibler divergence to quantify their divergence. The results show that the WINNER scenario groups (A, B, C, and D) or propagation classes (LoS, OLoS, and NLoS) do not necessarily ensure minimum separation within the groups/classes. Instead, the following grouping minimizes intragroup distances: (i) indoor-to-outdoor and outdoor-to-indoor scenarios (A2, B4, and C4), (ii) macrocell configurations for suburban, urban, and rural scenarios (C1, C2, and D1), and (iii) indoor/hotspot/microcellular scenarios (A1, B3, and B1). The computation of the divergence between Ilmenau and Dresden measurements and WINNER scenarios confirms that the parameters of the C2 scenario are a proper reference for a large variety of urban macrocell environments.

scholarly journals Quantitative unit classification of ventral tegmental area neurons in vivo

2012 ◽  
Vol 107 (10) ◽  
pp. 2808-2820 ◽  
Author(s):  
Wei Li ◽  
William M. Doyon ◽  
John A. Dani
Keyword(s):  
Ventral Tegmental Area ◽  
Quantitative Methods ◽  
Probability Distributions ◽  
Neural Population ◽  
Agglomerative Clustering ◽  
Ventral Tegmental ◽  
Type Receptor ◽  
High Firing

Neurons in the ventral tegmental area (VTA) synthesize several major neurotransmitters, including dopamine (DA), GABA, and glutamate. To classify VTA single-unit neural activity from freely moving rats, we used hierarchical agglomerative clustering and probability distributions as quantitative methods. After many parameters were examined, a firing rate of 10 Hz emerged as a transition frequency between clusters of low-firing and high-firing neurons. To form a subgroup identified as high-firing neurons with GABAergic characteristics, the high-firing classification was sorted by spike duration. To form a subgroup identified as putative DA neurons, the low-firing classification was sorted by DA D2-type receptor pharmacological responses to quinpirole and eticlopride. Putative DA neurons were inhibited by the D2-type receptor agonist quinpirole and returned to near-baseline firing rates or higher following the D2-type receptor antagonist eticlopride. Other unit types showed different responses to these D2-type receptor drugs. A multidimensional comparison of neural properties indicated that these subgroups often clustered independently of each other with minimal overlap. Firing pattern variability reliably distinguished putative DA neurons from other unit types. A combination of phasic burst properties and a low skew in the interspike interval distribution produced a neural population that was comparable to the one sorted by D2 pharmacology. These findings provide a quantitative statistical approach for the classification of VTA neurons in unanesthetized animals.

Stationary distributions under mutation-selection balance: structure and properties

1996 ◽  
Vol 28 (01) ◽  
pp. 227-251 ◽  
Author(s):  
Reinhard Bürger ◽  
Immanuel M. Bomze
Keyword(s):  
Existence And Uniqueness ◽  
Probability Distributions ◽  
Semigroup Theory ◽  
Complete Characterization ◽  
Stationary Distributions ◽  
Borel Measures ◽  
Classical Models ◽  
Balance Structure

A general model for the evolution of the frequency distribution of types in a population under mutation and selection is derived and investigated. The approach is sufficiently general to subsume classical models with a finite number of alleles, as well as models with a continuum of possible alleles as used in quantitative genetics. The dynamics of the corresponding probability distributions is governed by an integro-differential equation in the Banach space of Borel measures on a locally compact space. Existence and uniqueness of the solutions of the initial value problem is proved using basic semigroup theory. A complete characterization of the structure of stationary distributions is presented. Then, existence and uniqueness of stationary distributions is proved under mild conditions by applying operator theoretic generalizations of Perron–Frobenius theory. For an extension of Kingman's original house-of-cards model, a classification of possible stationary distributions is obtained.

Stationary distributions under mutation-selection balance: structure and properties

1996 ◽  
Vol 28 (1) ◽  
pp. 227-251 ◽  
Author(s):  
Reinhard Bürger ◽  
Immanuel M. Bomze
Keyword(s):  
Existence And Uniqueness ◽  
Probability Distributions ◽  
Semigroup Theory ◽  
Complete Characterization ◽  
Stationary Distributions ◽  
Borel Measures ◽  
Classical Models ◽  
Balance Structure

A general model for the evolution of the frequency distribution of types in a population under mutation and selection is derived and investigated. The approach is sufficiently general to subsume classical models with a finite number of alleles, as well as models with a continuum of possible alleles as used in quantitative genetics. The dynamics of the corresponding probability distributions is governed by an integro-differential equation in the Banach space of Borel measures on a locally compact space. Existence and uniqueness of the solutions of the initial value problem is proved using basic semigroup theory. A complete characterization of the structure of stationary distributions is presented. Then, existence and uniqueness of stationary distributions is proved under mild conditions by applying operator theoretic generalizations of Perron–Frobenius theory. For an extension of Kingman's original house-of-cards model, a classification of possible stationary distributions is obtained.

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