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Calculus for cognitive scientists = ...

Peterson, James K.

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Calculus for cognitive scientists = derivatives, integrals and models /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Calculus for cognitive scientists/ by James K. Peterson.
其他題名:	derivatives, integrals and models /
作者:	Peterson, James K.
出版者:	Singapore :Springer Singapore : : 2016.,
面頁冊數:	xxxi, 507 p. :ill., digital ;24 cm.
內容註:	Introductory Remarks -- Viability Selection -- Limits and Basic Smoothness -- Continuity and Derivatives -- Sin, Cos and All That -- Antiderivatives -- Substitutions -- Riemann Integration -- The Logarithm and Its Inverse -- Exponential and Logarithm Function Properties -- Simple Rate Equations -- Simple Protein Models -- Logistics Models -- Function Approximation -- Extreme Values -- Numerical Methods Order One ODEs -- Advanced Protein Models -- Matrices and Vectors -- A Cancer Model -- First Order Multivariable Calculus -- Second Order Multivariable Calculus -- Hamilton's Rule In Evolutionary Biology -- Final Thoughts -- Background Reading.
Contained By:	Springer eBooks
標題:	Calculus. -
電子資源:	http://dx.doi.org/10.1007/978-981-287-874-8
ISBN:	9789812878748$q(electronic bk.)

Calculus for cognitive scientists = derivatives, integrals and models /
Peterson, James K.

Calculus for cognitive scientistsderivatives, integrals and models /[electronic resource] :by James K. Peterson. - Singapore :Springer Singapore :2016. - xxxi, 507 p. :ill., digital ;24 cm. - Cognitive science and technology,2195-3988. - Cognitive science and technology..

Introductory Remarks -- Viability Selection -- Limits and Basic Smoothness -- Continuity and Derivatives -- Sin, Cos and All That -- Antiderivatives -- Substitutions -- Riemann Integration -- The Logarithm and Its Inverse -- Exponential and Logarithm Function Properties -- Simple Rate Equations -- Simple Protein Models -- Logistics Models -- Function Approximation -- Extreme Values -- Numerical Methods Order One ODEs -- Advanced Protein Models -- Matrices and Vectors -- A Cancer Model -- First Order Multivariable Calculus -- Second Order Multivariable Calculus -- Hamilton's Rule In Evolutionary Biology -- Final Thoughts -- Background Reading.

This book provides a self-study program on how mathematics, computer science and science can be usefully and seamlessly intertwined. Learning to use ideas from mathematics and computation is essential for understanding approaches to cognitive and biological science. As such the book covers calculus on one variable and two variables and works through a number of interesting first-order ODE models. It clearly uses MatLab in computational exercises where the models cannot be solved by hand, and also helps readers to understand that approximations cause errors - a fact that must always be kept in mind.

ISBN: 9789812878748$q(electronic bk.)

Standard No.: 10.1007/978-981-287-874-8doiSubjects--Topical Terms:

517463
Calculus.

LC Class. No.: QA303.2

Dewey Class. No.: 515

Calculus for cognitive scientists = derivatives, integrals and models /
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505 0 $a Introductory Remarks -- Viability Selection -- Limits and Basic Smoothness -- Continuity and Derivatives -- Sin, Cos and All That -- Antiderivatives -- Substitutions -- Riemann Integration -- The Logarithm and Its Inverse -- Exponential and Logarithm Function Properties -- Simple Rate Equations -- Simple Protein Models -- Logistics Models -- Function Approximation -- Extreme Values -- Numerical Methods Order One ODEs -- Advanced Protein Models -- Matrices and Vectors -- A Cancer Model -- First Order Multivariable Calculus -- Second Order Multivariable Calculus -- Hamilton's Rule In Evolutionary Biology -- Final Thoughts -- Background Reading.
520 $a This book provides a self-study program on how mathematics, computer science and science can be usefully and seamlessly intertwined. Learning to use ideas from mathematics and computation is essential for understanding approaches to cognitive and biological science. As such the book covers calculus on one variable and two variables and works through a number of interesting first-order ODE models. It clearly uses MatLab in computational exercises where the models cannot be solved by hand, and also helps readers to understand that approximations cause errors - a fact that must always be kept in mind.
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