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1

Nicolas, Christian. Random walk. London: Architectural Association Students Union, 1998.

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2

A random walk down Wall Street: The time-tested strategy for successful investing. New York: W.W. Norton & Co., 2011.

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3

A random walk down Wall Street: The time-tested strategy for successful investing. New York: W.W. Norton, 2012.

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4

A random walk down Wall Street: The time-tested strategy for successful investing. 9th ed. New York: W. W. Norton, 2007.

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5

Malkiel, Burton Gordon. A random walk down Wall Street: The time-tested strategy for successful investing. New York: W.W. Norton, 2003.

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6

Malkiel, Burton Gordon. A random walk down Wall Street: The time-tested strategy for successful investing. New York: W.W. Norton, 2003.

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7

A random walk down Wall Street: The time-tested strategy for successful investing. 9th ed. New York: W. W. Norton, 2007.

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8

A random walk down Wall Street: The time-tested strategy for successful investing. New York: W.W. Norton, 2003.

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9

A random walk down Wall Street: The time-tested strategy for successful investing. London: W.W.Norton, 2004.

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10

Luger, Richard. Exact non-parametric tests for a random walk with unknown drift under conditional heteroscedasticity. Ottawa, Ont: Bank of Canada, 2001.

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11

Luger, Richard. Exact non-parametric tests for a random walk with unknown drift under conditional heteroscedasticity. Ottawa, Ont: Bank of Canada, 2001.

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12

Economic time series with random walk and other nonstationary components. Amsterdam: Elsevier Science Publishers, 1988.

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13

Vulpiani, Angelo, Massimo Cencini, Andrea Puglisi, and Davide Vergni. Random Walk in Physics: Beyond Black Holes and Time-Travels. Springer International Publishing AG, 2021.

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14

Vulpiani, Angelo, Massimo Cencini, Andrea Puglisi, and Davide Vergni. Random Walk in Physics: Beyond Black Holes and Time-Travels. Springer International Publishing AG, 2022.

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15

A random walk down Wall Street: The time-tested strategy for successful investing. W.W. Norton & Company, 2016.

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16

A random walk down Wall Street: The time-tested strategy for successful investing. 2015.

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17

Sattarhoff, Cristina. Statistical Inference in Multifractal Random Walk Models for Financial Time Series. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2011.

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Sattarhoff, Cristina. Statistical Inference in Multifractal Random Walk Models for Financial Time Series. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2012.

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19

Malkiel, Burton G. Random Walk down Wall Street: The Time-Tested Strategy for Successful Investing. Norton & Company, Incorporated, W. W., 2011.

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20

Malkiel, Burton G. Random Walk down Wall Street: The Time-Tested Strategy for Successful Investing. Norton & Company, Incorporated, W. W., 2015.

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21

Malkiel, Burton G. Random Walk down Wall Street: The Time-Tested Strategy for Successful Investing. Norton & Company, Incorporated, W. W., 2007.

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22

Malkiel, Burton G. Random Walk down Wall Street: The Time-Tested Strategy for Successful Investing. Norton & Company, Incorporated, W. W., 2019.

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23

Random Walk down Wall Street: The Time-Tested Strategy for Successful Investing. Norton & Company, Incorporated, W. W., 2019.

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24

Random Walk down Wall Street - the Time-Tested Strategy for Successful Investing. Norton & Company Limited, W. W., 2023.

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25

Malkiel, Burton G. A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing. W. W. Norton & Company, 2020.

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26

Malkiel, Burton Gordon. A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing, Ninth Edition. 9th ed. W. W. Norton, 2007.

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27

Malkiel, Burton Gordon. A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing, Ninth Edition. W. W. Norton, 2007.

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28

Nosofsky, Robert M., and Thomas J. Palmeri. An Exemplar-Based Random-Walk Model of Categorization and Recognition. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.7.

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Abstract:
In this chapter, we provide a review of a process-oriented mathematical model of categorization known as the exemplar-based random-walk (EBRW) model (Nosofsky & Palmeri, 1997a). The EBRW model is a member of the class of exemplar models. According to such models, people represent categories by storing individual exemplars of the categories in memory, and classify objects on the basis of their similarity to the stored exemplars. The EBRW model combines ideas ranging from the fields of choice and similarity, to the development of automaticity, to response-time models of evidence accumulation and decision-making. This integrated model explains relations between categorization and other fundamental cognitive processes, including individual-object identification, the development of expertise in tasks of skilled performance, and old-new recognition memory. Furthermore, it provides an account of how categorization and recognition decision-making unfold through time. We also provide comparisons with some other process models of categorization.
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