scholarly journals Discrete regimes of information reception in non-antagonistic repeated game

2020 ◽  
Vol 12 (1) ◽  
pp. 3-18
Author(s):  
Елена Захаровна Мохонько ◽  
Elena Mokhonko
Keyword(s):  
Repeated Game ◽  
Current Information ◽  
Sample Data ◽  
True Variant

The gain functions depend on the choices of players and time. The set of choices of the second player is changed in time according to one of some variants. The true variant is ascertained during the game. The current information about the set of choices and about partner's choices is received as sample data. An optimal discrete procedure of obtaining information is found that allows preserving the equilibrium.

Reversion Toward the Mean Independently of Regression Toward the Mean

Methodology ◽  
2009 ◽  
Vol 5 (1) ◽  
pp. 3-6 ◽  
Author(s):  
Merton S. Krause
Keyword(s):  
Random Error ◽  
Evaluation Studies ◽  
Internal Validity ◽  
Intervention Evaluation ◽  
Population Mean ◽  
Experimental Intervention ◽  
Sample Data ◽  
The Mean ◽  
Regression Toward The Mean

There is another important artifactual contributor to the apparent improvement of persons subjected to an experimental intervention which may be mistaken for regression toward the mean. This is the phenomenon of random error and extreme selection, which does not at all involve the population regression of posttest on pretest scores but involves a quite different and independent reversion of subjects’ scores toward the population mean. These two independent threats to the internal validity of intervention evaluation studies, however, can be detected and differentiated on the sample data of such studies.

The Animal Models for Hemorrhage and Thrombosis in the Neonate

1987 ◽  
Vol 57 (01) ◽  
pp. 118-122 ◽  
Author(s):  
C Thomas Kisker
Keyword(s):  
Animal Models ◽  
Normal Development ◽  
Relevant Information ◽  
Relevant Model ◽  
Current Information ◽  
Single Animal ◽  
Hemostatic Disorders ◽  
Thrombotic Diseases

SummaryAnimal models have added significantly to our understanding of adult hemorrhagic and thrombotic diseases. Few models, however, have been developed for studies of the hemostatic disorders in the fetus and newborn. This report reviews the current information on animal models of fetal and neonatal hemostasis. The requirements of a relevant model are addressed and previous studies using fetal and neonatal animal models are reviewed. A recommendation of a single animal for all studies of fetal and neonatal hemostasis is not possible. However, the lamb has been the most frequently studied and appears to provide relevant information regarding normal development and the factors which may adversely influence hemostasis in the fetus and newborn.

METHODS OF CONSULTING ON STUDYING THE DEGREE OF MOISTURE SUPPLY OF SLOPE LANDS FOR AGRICULTURAL PRODUCTION IN AZERBAIJAN

2018 ◽  
Author(s):  
Prof.RAE Aliev Z.H.
Keyword(s):  
Key Words ◽  
Agricultural Production ◽  
Soil Ecology ◽  
Arable Soil ◽  
Clean Technology ◽  
Agricultural Product ◽  
Current Information ◽  
Moisture Supply ◽  
Managerial System

The current information on moisture and the temperature of the ground in managerial system by production to agricultural product necessary, in the first place, for taking the operative decisions at development ecological clean technology irrigation under growing agricultural cultures to achieve the maximum harvest. Key words: aerospace methods, COW, moisture, moisture test, arable, soil, ecology, vafer humidity, drill, graduation, tool, etc.

Delivering Sustainable Water Systems Using Kalman Filter and Statistical Approach

2017 ◽  
Vol 4 (1) ◽  
pp. 41-52
Author(s):  
Dedy Loebis
Keyword(s):  
Kalman Filtering ◽  
Statistical Approach ◽  
Water Systems ◽  
Data Sets ◽  
Pressure Data ◽  
Burst Event ◽  
Sample Data ◽  
Other Information ◽  
Supply Systems ◽  
Frequency Features

This paper presents the results of work undertaken to develop and test contrasting data analysis approaches for the detection of bursts/leaks and other anomalies within wate r supply systems at district meter area (DMA)level. This was conducted for Yorkshire Water (YW) sample data sets from the Harrogate and Dales (H&D), Yorkshire, United Kingdom water supply network as part of Project NEPTUNE EP/E003192/1 ). A data analysissystem based on Kalman filtering and statistical approach has been developed. The system has been applied to the analysis of flow and pressure data. The system was proved for one dataset case and have shown the ability to detect anomalies in flow and pres sure patterns, by correlating with other information. It will be shown that the Kalman/statistical approach is a promising approach at detecting subtle changes and higher frequency features, it has the potential to identify precursor features and smaller l eaks and hence could be useful for monitoring the development of leaks, prior to a large volume burst event.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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