Taylors Theorem: Taylor’s theorem gives an approximation of a n times differentiable function around a given point by a n-th order Taylor-polynomial. For analytic functions the Taylor polynomials at a given point are fixed order truncations of its Taylor’s series‚ which completely determines the function in some locality of the point. There are numerous forms of it applicable in different situations‚ and some of them contain explicit estimates on the approximation error of the function by its Taylor-polynomial
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http://www.thebravesandsmarts.com/2013/02/the-great-indian-mathematician.html http://www-history.mcs.st-and.ac.uk/Biographies/Aryabhata_I.html (Please check this website‚ I was unable to copy the information) Āryabhaṭa (Devanāgarī: आर्यभट) (AD 476 – 550) is the first of the great mathematician-astronomers of the classical age of Indian mathematics and Indian astronomy. He was born at Muziris (the modern day Kodungallour village) near Thrissur‚ Kerala. Available evidence suggest that he went
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Fundamental Mathematics-EEIT 2011 Lecturer: Dr. Nguyen Minh Quan. Office Hours: Tuesday‚ 10:00 am – 11:30 am. Room 105 Email: nmquan05@gmail.com Class’s email: fund.math.vgu@gmail.com. Password: fundmath2012 Lectures: Tuesday 1:00 pm - 4: 15pm. TA and exercise class: To be announced. Textbooks: J. Stewart‚ Calculus early transcendentals 6th ed.‚ Brooks/Cole Pub Co‚ 2008. The course will cover Chapters 1 through 9. References (optional): J. Rogawski‚ Calculus‚ Early Transcendentals‚ W.H
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Derivation of “quick and dirty” approximation of bias formula Thomas Noe Balliol College/SBS 21st October‚ 2013 This note relates to the derivation of the “quick and dirty” formula for estimating the bias generated by using the YTM as an approximation of the expected return on debt. The assumptions: 1. Debt is perpetual 2. probability of default is δ in each period. The probability is the same in every period 3. If default occurs‚ bondholders receive ρ fraction of the face (principal) value
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#1) Shaping (also known as the successive approximation)‚ in psychology‚ is linked to a behavioral procedure widely referred to as operant training. During shaping‚ the animal elicits consequential behavior‚ resultant of the trained desired response during a series of reinforcing trials to emulate the objective behavioral response. In other words‚ shaping assists experimenters in setting particular goals to reach a preferred behavior. For instance‚ while attempting to shape Sniffy’s
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4CursSemantica Componential Analysis Classical structuralism 0. Preliminaries From previous study‚ it is known that linguistic analysis proceeds level by level‚ specifying in each case: the primitives of the level‚ the combinatorial operations and rules‚ and finally a representation of the utterance on that level. In the particular case of the semantic level‚ one must specify: a) the sense components ‚ the constructional rules for building complex meaning out of the more elementary
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2 Approach l l Constraint Based Reasoning – Distributed Constraint Optimization Problem (DCOP) Adopt algorithm – First-ever distributed‚ asynchronous‚ optimal algorithm for DCOP – Efficient‚ polynomial-space l Bounded error approximation – Principled solution-quality/time-to-solution tradeoffs 3 Constraint Representation Why constraints for multiagent systems? l Constraints are natural‚ general‚ simple – Many successful applications l l Leverage existing work in AI
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Subject : Probability and Statistics = PS Strand 1: Introduction to Statistics. Strand 2: Organizing Data. Strand 3 : Averages and Variation Strand 4: Elementary Probability Theory. Strand 5: The Binomial Probability Distribution and Related Topics. Strand 6: Normal Distributions. Strand 7: Introduction to Sample Distributions. Benchmark Code Subject (M‚ S‚ SS‚ LA).Grade#.Strand#.Standard#. Benchmark# Example: PS.1.4.3 – Probability and Statistics‚ Strand 1‚ Standard 4‚ Benchmark 3 Strand: 1 INTRODUCTION
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central The Central Limit Theorem A long standing problem of probability theory has been to find necessary and sufficient conditions for approximation of laws of sums of random variables. Then came Chebysheve‚ Liapounov and Markov and they came up with the central limit theorem. The central limit theorem allows you to measure the variability in your sample results by taking only one sample and it gives a pretty nice way to calculate the probabilities for the total ‚ the average and the proportion
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History of Mathematics Compare and contrast mathematics in India and China in the period 213 BCE to 1425 CE. India and China prepared the main contributions in the past for mathematics that has influenced mathematics in today’s day and age‚ with numerous discoveries that would inspire the world of mathematics to an unimaginable degree. The period 213 BCE and 1425 CE is important to examine just because we believe that this was the approximate time of the Buddhist missionaries‚ were they travelled
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