Different probability distribution

Part 2 book "Engineering mathematics" includes content: Techniques of integration; applications of integration; further topics in integration, numerical integration; Taylor polynomials, Taylor series and Maclaurin series, ordinary differential equations; ordinary differential equations; the Laplace transform; difference equations and the Z transform; fourier series; the Fourier transform; functions of several variables; vector calculus; line integrals and multiple integrals; probability; statistics and probability distributions.

548p muasambanhan06 01-02-2024 3 2   Download

In paper "Mixture of Erlang and exponential approximation for ultimate ruin probability", we investigate the mixture of Erlang and exponential approximation based on the first three moments and the matrix-exponential representation of mixed Erlang and exponential functions. Besides, a number of numerical examples are given to illustrate the quick convergence of the method in the cases of the first three moments of uniform distribution on . Moreover to illustrate this method, a numerical example is given with different initial reserves of insurance company.

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This study also analyzed different scenarios and noticed that the total loss of the distribution decreased by increasing the number of BESS. The result reveals that with the optimum BESS size achieved, it can dramatically improve the stability of the microgrid and simultaneously maintain a high level of economic viability.

5p vifalcon 16-05-2023 10 5   Download

Lecture Probability Theory - Lecture 5: Function of a Random Variable. Let Y be a random variable, discrete and continuous, and let g be a function from R to R, which we think of as a transformation. For example, Y could be a height of a randomly chosen person in a given population in inches, and g could be a function which transforms inches to centimeters, i.e. g(y) = 2.54 × y. Then W = g(Y) is also a random variable, but its distribution (pdf), mean, variance, etc. will differ from that of Y.

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In this paper, a novel privacy preserving method for data publication is proposed based on conditional probability distribution and machine learning techniques, which can achieve different prior beliefs for different transactions.

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Insertions and deletions (indels) account for more nucleotide differences between two related DNA sequences than substitutions do, and thus it is imperative to develop a method to reliably calculate the occurrence probabilities of sequence alignments via evolutionary processes on an entire sequence.

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ERp is a variable selection and classification method for metabolomics data. ERp uses minimized classification error rates, based on data from a control and experimental group, to test the null hypothesis of no difference between the distributions of variables over the two groups.

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In Odisha the Mahanadi is the largest river with an extensive delta responsible for most of the devastating flood hazards in the coastal zone. Over a period of 146 years between 1855 and 2000 there were 28 high flood years, 57 medium flood years and 48 low flood years (Mishra, 2008). There is a need to study the peak flood magnitude at different probability of exceedences by different probability distribution functions.

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The knowledge of probable maximum one day rainfall for a given region corresponding to return periods varying from 2 to 100 years is essential for crop planning and designs of minor and major hydraulic structures. The probability analysis for maximum one day rainfall of Khurda region is done by different probability distribution methods (log normal 3-parameter, pearson method, log pearson, weibull, generalized pare to distribution and log normal) by taking the rainfall data of 25 years(1991-2015) through FLOOD - frequency analysis software.

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The present paper follows the same approach as that of Turner and Bowen (1999). The Multinomial regression is specified as P ðMi ¼ jÞ¼ðexpðbj XiÞ= P5 j1 expðbj XiÞÞ, where P (Mi¼ j) denotes the probability of choosing outcome j, the particular course/major choice that categorizes different disciplines. This response variable is specified with five categories: such as medicine, engineering, other professional courses, science and humanities.

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Distribution of Rainfall plays important role in agriculture and efficient utilization of the water resources. The present study was conducted to know the distribution of rainfall during Monsoon period of different districts of Karnataka. The secondary data of Rainfall over a period of 34 years (1980- 2013) was collected from AICRP on Agro Meteorology, UAS Bangalore. Around 20 different probability distributions were used to evaluate the best fit for maximum daily rainfall (mm).

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Rainfall intensity, duration and its distribution play a major role in the growth of agriculture and other related sectors and the overall development of a country. The present study is carried out to know the best fitting probability distribution for rainfall data in three different taluks of Kodagu District.

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The daily rainfall data of 21 years were collected from the IMD approved Meteorological Observatory situated at Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, India. The data were then processed to identify the maximum rainfall received on any one day (24 hrs duration), in any week (7 days), in a month (4 weeks), in a monsoon season (4 months) and in a year (365 days period). The data showed that the annual daily maximum rainfall received at any time ranged between 134.5 (minimum) to 694.6 mm (Maximum) indicating a very large range of fluctuation during the period of study.

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Rainfall is a prime input for various engineering design such as hydraulic structures, water conservation structures, bridges and culverts, canals, storm water sewer and road drainage system. The detailed statistical analysis of each region is essential to estimate the relevant input value for design and analysis of engineering structures and also for crop planning. The present study comprises statistical analysis i.e. frequency analysis of daily maximum rainfall data of Udaipur district.

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Different continuous probability distribution was used to characterize the annual rainfall of Yadgir district. The best fitted distributions for the annual rainfall data are Weibull (3P), GEV, Gamma (3P) and Gumbel based on KS–test. Nearly more than 70% of annual rainfall received from south monsoon (kharif season), the best fitted probability distribution for the period of south west monsoon are Weibull (2P), GEV, Gamma (3P), and Weibull (3P) based on KS-test.

15p kethamoi2 15-12-2019 14 0   Download

The primary need of water resource development in any area depends on estimation of rainfall at different probabilities for efficient planning and design of irrigation and drainage systems, command area development, soil and water conservation programmes and the optimum utilization of water resources in various agricultural production systems. The annual maximum daily rainfall data of 18 years (1991 to 2008) was obtained from ARS, Mulde. It was analyzed for maximum one day and extended days (up to 6 day) rainfall for Mulde.

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The paper present: method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learning a (null hypothesis) probability model based on a large set of domain names that have been white listed by some reliable authority.

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This paper presents an approach of achieving adaptive e-learning by probabilistically evaluating a learner based not only on the profile and performance data of the learner but also on the data of previous learners. In this approach, an adaptation rule specification language and a user interface tool are provided to a content author or instructor to define adaptation rules. The defined rules are activated at different stages of processing the learning activities of an activity tree which models a composite learning object.

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The paper presents the results of a detailed packet-level simulation comparing three multi-hop wireless ad hoc network routing protocols under the load of different probability distributions, that cover a range of design choices having different protocol viz. AODV,DSR and GRP.

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Chapter 5 - Discrete random variables. After mastering the material in this chapter, you will be able to: Explain the difference between a discrete random variable and a continuous random variable, find a discrete probability distribution and compute its mean and standard deviation, use the binomial distribution to compute probabilities,...

12p whocare_b 05-09-2016 63 2   Download