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Books on the topic 'Measure metric space'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Nicola, Gigli, and Savaré Giuseppe, eds. Gradient flows: In metric spaces and in the space of probability measures. Boston: Birkhäuser, 2005.

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2

Ambrosio, Luigi. Gradient flows: In metric spaces and in the space of probability measures. Basel: Birkhauser, 2004.

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3

Nicola, Gigli, Savaré Giuseppe, Struwe Michael 1955-, and SpringerLink (Online service), eds. Gradient Flows: In Metric Spaces and in the Space of Probability Measures. Basel: Birkhäuser Basel, 2008.

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4

Shirali, Satish. A Concise Introduction to Measure Theory. USA: Springer International Publishing AG, 2019.

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5

Probability measures on metric spaces. Providence, R.I: AMS Chelsea Pub., 2005.

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6

Fremlin, D. H. Measure-additive coverings and measurable selectors. Warszawa: Państwowe Wydawn. Naukowe, 1987.

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7

Barlow, Martin T. Stability of parabolic Harnack inequalities on metric measure spaces. Kyoto, Japan: Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2004.

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8

1951-, Domínguez Benavides T., and López Acedo G. 1956-, eds. Measures of noncompactness in metric fixed point theory. Basel: Birkhäuser Verlag, 1997.

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9

Carlsson, Niclas. Markov chains on metric spaces: Invariant measures and asymptotic behaviour. Åbo: Åbo Akademi University Press, 2005.

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10

Chen, Zhen-Qing. Heat Kernel Estimates for Jump Processes of Mixed Types on Metric Measure Spaces. Kyoto, Japan: Research Institute for Mathematical Sciences, Kyoto University, 2006.

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11

Billingsley, Patrick. Convergence of Probability Measures. 2nd ed. New York, USA: Wiley-Interscience, 1999.

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12

1962-, Semmes Stephen, ed. Fractured fractals and broken dreams: Self-similar geometry through metric and measure. Oxford: Clarendon Press, 1997.

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13

1983-, Spadaro Emanuele Nunzio, ed. Q-valued functions revisited. Providence, R.I: American Mathematical Society, 2010.

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14

1968-, Dafni Galia Devora, McCann Robert John 1968-, and Stancu Alina 1968-, eds. Analysis and geometry of metric measure spaces: Lecture notes of the 50th Séminaire de Mathématiques Supérieures (SMS), Montréal, 2011. Providence, Rhode Island, USA: American Mathematical Society, 2013.

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15

Mitrea, Dorina. Groupoid Metrization Theory: With Applications to Analysis on Quasi-Metric Spaces and Functional Analysis. Boston: Birkhäuser Boston, 2013.

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16

Ecole d'été de probabilités de Saint-Flour (35th : 2005), ed. Probability and real trees: École d'Été de Probabilités de Saint-Flour XXXV-2005. Berlin: Springer, 2008.

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17

Gradient Flows: In Metric Spaces and in the Space of Probability Measures (Lectures in Mathematics. ETH Zürich (closed)). Birkhäuser Basel, 2006.

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18

Yeh, J. Metric in Measure Spaces. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/10289.

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19

Metric In Measure Spaces. New Jersey, USA: World Scientific Pub Co Inc, 2020.

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20

Koskela, Pekka, Jeremy T. Tyson, Nageswari Shanmugalingam, and Juha Heinonen. Sobolev Spaces on Metric Measure Spaces. Cambridge University Press, 2015.

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21

Lukacs, E., K. R. Parthasarathy, and Z. W. Birnbaum. Probability Measures on Metric Spaces. Elsevier Science & Technology Books, 2014.

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22

Gauge Integrals over Metric Measure Spaces. Saarbrücken, German: Scholars' Press, 2014.

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23

Koskela, Pekka, Jeremy T. Tyson, Nageswari Shanmugalingam, and Juha Heinonen. Sobolev Spaces on Metric Measure Spaces: An Approach Based on Upper Gradients. Cambridge University Press, 2015.

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24

Koskela, Pekka, Jeremy T. Tyson, Nageswari Shanmugalingam, and Juha Heinonen. Sobolev Spaces on Metric Measure Spaces: An Approach Based on Upper Gradients. Cambridge University Press, 2015.

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25

Koskela, Pekka, Jeremy T. Tyson, Nageswari Shanmugalingam, and Juha Heinonen. Sobolev Spaces on Metric Measure Spaces: An Approach Based on Upper Gradients. Cambridge University Press, 2015.

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26

Koskela, Pekka, Jeremy T. Tyson, Nageswari Shanmugalingam, and Juha Heinonen. Sobolev Spaces on Metric Measure Spaces: An Approach Based on Upper Gradients. Cambridge University Press, 2015.

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27

Billingsley, Patrick. Convergence of Probability Measures. Wiley & Sons, Incorporated, John, 2009.

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28

Billingsley, Patrick. Convergence of Probability Measures. Wiley & Sons, Incorporated, John, 2013.

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29

Billingsley, Patrick. Convergence of Probability Measures. Wiley & Sons, Incorporated, John, 2013.

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30

Gigli, Nicola. On the Differential Structure of Metric Measure Spaces and Applications. American Mathematical Society, 2015.

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31

Mondino, Andrea, Luigi Ambrosio, and Giuseppe Savare. Nonlinear Diffusion Equations and Curvature Conditions in Metric Measure Spaces. American Mathematical Society, 2020.

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32

Basic Analysis IV: Measure Theory and Integration. Taylor & Francis Group, 2020.

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33

Peterson, James K. Basic Analysis IV: Measure Theory and Integration. Taylor & Francis Group, 2020.

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34

Basic Analysis IV: Measure Theory and Integration. Taylor & Francis Group, 2020.

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35

Peterson, James K. Basic Analysis IV: Measure Theory and Integration. Taylor & Francis Group, 2020.

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36

Peterson, James K. Basic Analysis IV: Measure Theory and Integration. Taylor & Francis Group, 2020.

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37

Limit Theorems For Nonlinear Cointegrating Regression. Singapore, Hong Kong: WPSC, 2015.

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38

David, Guy, and Stephen Semmes. Fractured Fractals and Broken Dreams: Self-Similar Geometry through Metric and Measure (Oxford Lecture Series in Mathematics and Its Applications, No 7). Oxford University Press, USA, 1998.

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39

Billingsley, Patrick. Convergence of Probability Measures. Wiley & Sons, Incorporated, John, 2007.

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40

Farb, Benson, and Dan Margalit. Teichmuller Geometry. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691147949.003.0012.

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This chapter focuses on the metric geometry of Teichmüller space. It first explains how one can think of Teich(Sɡ) as the space of complex structures on Sɡ. To this end, the chapter defines quasiconformal maps between surfaces and presents a solution to the resulting Teichmüller's extremal problem. It also considers the correspondence between complex structures and hyperbolic structures, along with the Teichmüller mapping, Teichmüller metric, and the proof of Teichmüller's uniqueness and existence theorems. The fundamental connection between Teichmüller's theorems, holomorphic quadratic differentials, and measured foliations is discussed as well. Finally, the chapter describes the Grötzsch's problem, whose solution is tied to the proof of Teichmüller's uniqueness theorem.
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41

Brauner, Marygail, and Arthur W. Lackey. CWT and RWT Metrics Measure the Performance of the Army's Logistics Chain for Spare Parts. RAND Corporation, 2003. http://dx.doi.org/10.7249/rb3035.

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42

Mitrea, Dorina, Marius Mitrea, and Irina Mitrea. Groupoid Metrization Theory: With Applications to Analysis on Quasi-Metric Spaces and Functional Analysis. Springer, 2012.

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43

Elements Of Real Analysis: Pure and Applied Mathematics. Boca Raton, Florida, USA: Chapman & Hall/ CRC, 2006.

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44

Boules, Adel N. Fundamentals of Mathematical Analysis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198868781.001.0001.

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Fundamentals of Mathematical Analysis is a beginning graduate textbook on real and functional analysis, with a substantial component on topology. The three leading chapters furnish background information on the real and complex number fields, a concise introduction to set theory, and a rigorous treatment of vector spaces. Instructors can choose material from this part as their students’ background warrants. Chapter 4 is the spine of the book and is essential for an effective reading of the rest of the book. It is an extensive study of metric spaces, including the core topics of completeness, compactness, and function spaces, with a good number of applications. The remaining chapters consist of an introduction to general topology, a classical treatment of Banach and Hilbert spaces, the elements of operator theory, and a deep account of measure and integration theories. Several courses can be based on the book. The entire book is suitable for a two-semester course on analysis, and material can be chosen to design one-semester courses on topology, real analysis, or functional analysis. The book is designed as an accessible classical introduction to the subject, aims to achieve excellent breadth and depth, and contains an abundance of examples and exercises. The topics are carefully sequenced, the proofs are detailed, and the writing style is clear and concise. The only prerequisites assumed are a thorough understanding of undergraduate real analysis and linear algebra, and a degree of mathematical maturity.
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45

Abstract Duality Pairs In Analysis: (Functional Analysis). Hackensack, New Jersey, USA: World Scientific Publishing Company, 2018.

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46

Evans, Steven N. Probability and Real Trees: École d'Été de Probabilités de Saint-Flour XXXV-2005. Springer London, Limited, 2007.

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