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Books on the topic 'STEEP PROBABILITY'

Author: Grafiati

Published: 11 September 2023

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1

Ronchetti, Elvezio. Step 1: Statistique et probabilités : une introduction. 2nd ed. Lausanne: Presses polytechniques et universitaires romandes, 1991.

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2

Fowler, C. W. Constructing species frequency distributions: A step toward systemic management. [Seattle, Wash.]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center, 1999.

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3

M, Kerry Sally, ed. Presenting medical statistics: From proposal to publication : a step-by-step guide. Oxford: Oxford University Press, 2007.

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4

B, Schlegelmilch Bodo, ed. Taking the fear out of data analysis: A step-by-step approach. London: Dryden Press, 1997.

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5

Diamantopoulos, Adamantios. Taking the fear out of data analysis: A step-by-step approach. [London]: Business Press, 2000.

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6

Diamantopoulos, Adamantios. Taking the fear out of data analysis: A step-by-step approach. London: Thomson Learning, 2000.

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7

SPSS survival manual: A step by step guide to data analysis using SPSS for Windows. 3rd ed. Maidenhead, England: McGraw Hill/Open University Press, 2007.

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8

SPSS survival manual: A step by step guide to data analysis using SPSS for Windows (version 12). 2nd ed. Maidenhead, Berkshire. U.K: Open University Press, 2005.

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9

Pallant, Julie. SPSS survival manual: A step by step guide to data analysis using SPSS for WIndows (versions 10 and 11). Maidenhead: Open University Press, 2001.

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10

Ma, Jin. Forward-backward stochastic differential equations and their applications. Berlin: Springer, 1999.

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11

Saha, Prasenjit, and Paul A. Taylor. Nuclear Fusion in Stars. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198816461.003.0006.

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This chapter enumerates some of the many nuclear reactions in stars. It focuses on a general principle: nuclear fusion requires overcoming the Coulomb barrier between nuclei, which is possible through the relatively infrequent process of quantum tunnelling. The tunnelling probability depends on the atomic num-bers and mass numbers of the nuclei involved, and also on their relative speed. These translate into steep and interesting temperature dependencies for nuclear reactions. Analytic approximations yield a rate that is almost vanishingly rare, yet within the incredibly large number of potential interactions within stars quantum tunnelling can still provide the underlying ignition of nuclear reactions.

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12

Publishing, School Specialty. Step-By-Step Probability, Intermediate. Instructional Fair, 2003.

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13

Gelman, Andrew, and Deborah Nolan. Problems and projects in probability. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0019.

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When probability is taught as a separate unit, not as part of a statistics course, then it makes sense to include exercises and examples that are motivated from mathematics rather than from applications. This chapter illustrates one way to promote active student involvement in a more advanced class. The material is designed to accommodate various mathematical backgrounds and to challenge all students. There are essentially three basic aspects to our mathematical probability courses: we teach in an interactive seminar style; we give students many challenging problems; and we require students to work on longer projects where they derive complicated results step by step. This chapter provides examples of many different kinds of problems, such as challenging questions on Buffon’s needle and noodle, a structured project on arcsine laws, an unstructured project on random permutations, and reading research paper in probability.

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14

Foreman, Dale I., and Gregory W. Corder. Nonparametric Statistics: A Step-by-Step Approach. Wiley, 2014.

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15

Foreman, Dale I., and Gregory W. Corder. Nonparametric Statistics: A Step-by-Step Approach. Wiley & Sons, Incorporated, John, 2014.

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16

Foreman, Dale I., and Gregory W. Corder. Nonparametric Statistics: A Step-By-Step Approach. Wiley & Sons, Incorporated, John, 2014.

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17

Foreman, Dale I., and Gregory W. Corder. Nonparametric Statistics: A Step-By-Step Approach. Wiley & Sons, Incorporated, John, 2014.

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18

Foreman, Dale I., and Gregory W. Corder. Nonparametric Statistics: A Step-By-Step Approach. Wiley & Sons, Incorporated, John, 2014.

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19

Foreman, Dale I., and Gregory W. Corder. Nonparametric Statistics: A Step-By-Step Approach. Wiley & Sons, Incorporated, John, 2014.

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20

Wanklyn. Stainless steel waste containers: An assessment of the probability of stress cracking. 1991.

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21

Boyer, Mr Dennis. Money Charts : 8 High-Probability Trading Setups: Step-by-Step Chart-Based Trading for Any Market. Independently published, 2019.

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22

Borodin, Alexei, and Leonid Petrov. Integrable probability: stochastic vertex models and symmetric functions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0002.

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This chapter presents the study of a homogeneous stochastic higher spin six-vertex model in a quadrant. For this model concise integral representations for multipoint q-moments of the height function and for the q-correlation functions are derived. At least in the case of the step initial condition, these formulas degenerate in appropriate limits to many known formulas of such type for integrable probabilistic systems in the (1+1)d KPZ universality class, including the stochastic six-vertex model, ASEP, various q-TASEPs, and associated zero-range processes. The arguments are largely based on properties of a family of symmetric rational functions that can be defined as partition functions of the higher spin six-vertex model for suitable domains; they generalize classical Hall–Littlewood and Schur polynomials. A key role is played by Cauchy-like summation identities for these functions, which are obtained as a direct corollary of the Yang–Baxter equation for the higher spin six-vertex model.

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23

Donovan, Therese, and Ruth M. Mickey. Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.001.0001.

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Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journey, from introductory probability to Bayesian inference and applications, including Markov Chain Monte Carlo approaches for parameter estimation, Bayesian belief networks, and decision trees. Detailed examples in each chapter contribute a great deal, where Bayes’ Theorem is at the front and center with transparent, step-by-step calculations. A vast amount of material is covered in a lighthearted manner; the journey is relatively pain-free. The book is intended to jump-start a reader’s understanding of probability, inference, and statistical vocabulary that will set the stage for continued learning. Other features include multiple links to web-based material, an annotated bibliography, and detailed, step-by-step appendices.

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24

Chigozie, Godwin. Probability Essentials and Graphic Guide to Multivariate Data Analysis: A Step-By-Step Guide to Data Analysis and Interpretation for Healthcare Practitioners Using SPSS. Independently Published, 2020.

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25

Peacock, Janet, and Sally Kerry. Presenting Medical Statistics from Proposal to Publication: A Step-by-step Guide (Oxford Medical Publications). Oxford University Press, USA, 2006.

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26

Adebiyi, Babatope. Complete Start Trading Like Pro Today : 5 in 1 Book to Learn How to Trade Forex Step by Step: Learn High Probability Trading Strategies to Make Maximum Profits in Forex. Independently Published, 2022.

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27

Yeske, Dave, and Elissa Buie. Psychological Aspects of Financial Planning. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190269999.003.0015.

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This chapter discusses personal financial planning, which is an interdisciplinary practice that employs a six-step process to develop integrated strategies for individuals and families to efficiently mobilize their human and financial capital to achieve their life goals. Financial planning draws from various disciplines, including counseling, psychology, finance, economics, and law. It includes budgeting and cash flow planning, risk management, insurance planning, investment planning, retirement and employee benefits planning, tax planning, and estate planning. The strategic process whereby financial planners develop integrated strategies that draw from all these fields in pursuit of client goals is the profession’s unique domain. Heuristics and mental biases to which clients may be prone overlay the entire financial planning process, however. Financial planners should understand and consider these issues when developing recommendations uniquely suited to each client, maximizing the probability that the client will embrace and implement the recommended strategies.

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28

Bandyopadhyay, Arindam. Basic Statistics for Risk Management in Banks and Financial Institutions. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849014.001.0001.

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The book provides an engaging account of theoretical, empirical, and practical aspects of various statistical methods in measuring risks of financial institutions, especially banks. In this book, the author demonstrates how banks can apply many simple but effective statistical techniques to analyse risks they face in business and safeguard themselves from potential vulnerability. It covers three primary areas of banking risks—credit, market, and operational risk, and in a uniquely intuitive, step-by-step manner, the author provides hands-on details on the primary statistical tools that can be applied for financial risk measurement and management. The book lucidly introduces concepts of various well-known statistical methods such as correlations, regression, matrix approach, probability and distribution theorem, hypothesis testing, Value at Risk (Vary), and Monte Carlo simulation techniques and provides a hands-on estimation and interpretation of these tests in measuring risks of the financial institutions. The books strike a fine balance between concepts and mathematics to tell a rich story of thoughtful use of statistical methods. The book will be of much interest to academics, risk managers, bankers, and consultants and general readers too. It emphasizes on specific risk measurement tools and techniques with data applications, templates required for data collection and analysis, numerous excel-based illustrations as well as analysis in econometric packages. Excel-based hands-on and use of econometric packages like STATA, EVIEWS, and @RISK will help practitioners, academia, and students to connect theory with application.

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29

R Deep Learning Essentials: A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet, 2nd Edition. Packt Publishing, 2018.

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30

Ma, Jin, and Jiongmin Yong. Forward-Backward Stochastic Differential Equations and their Applications (Lecture Notes in Mathematics). Springer, 2007.

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31

Yong, Jiongmin, and Jin Ma. Forward-Backward Stochastic Differential Equations and Their Applications. Springer London, Limited, 2007.

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