**Understanding Inferential Statistics: From A for Significance Test to Z for Confidence Interval by Markus Janczyk, Roland Pfister**

English | October 25, 2023 | ISBN: 3662667851 | 220 pages | MOBI | 12 Mb

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English | October 25, 2023 | ISBN: 3662667851 | 220 pages | MOBI | 12 Mb

English | 2024 | ISBN: 9819744377 | 179 Pages | PDF EPUB (True) | 31 MB

English | 2024 | ISBN: 9819999936 | 211 Pages | PDF EPUB (True) | 17 MB

English | 2019 | ISBN: 1119487846 | PDF | pages: 1215 | 24.4 mb

English | 2024 | ISBN: 3031642724 | 315 Pages | PDF EPUB (True) | 43 MB

English | 2008 | pages: 77 | ISBN: 1599943166 | PDF | 1,9 mb

English | 2018 | ISBN: 1138707643, 1138707627 | EPUB | pages: 228 | 0.6 mb

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | 1 hours 3 minutes | 16 lectures | 303.82 MB

SAS for Statistical Concepts and Data Visualization

English | 2008 | pages: 654 | ISBN: 1568274025 | PDF | 5,1 mb

English | 2024 | ISBN: 3031632419 | 318 Pages | PDF EPUB (True) | 31 MB

English | PDF (True) | 2005 | 511 Pages | ISBN : 1852337788 | 5.1 MB

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.

English | 2024 | ISBN: 9819721350 | 442 Pages | PDF EPUB (True) | 63 MB

English | PDF | 2005 | 355 Pages | ISBN : 0387212922 | 2 MB

This work is aimed at an audience with a sound mathematical background wishing to learn about the rapidly expanding ?eld of mathematical ?nance. Its content is suitable particularly for graduate students in mathematics who have a background in measure theory and probability. The emphasis throughout is on developing the mathematical concepts required for the theory within the context of their application. No attempt is made to cover the bewildering variety of novel (or ‘exotic’) ?nancial - struments that now appear on the derivatives markets; the focus throu- out remains on a rigorous development of the more basic options that lie at the heart of the remarkable range of current applications of martingale theory to ?nancial markets. The ?rst ?ve chapters present the theory in a discrete-time framework. Stochastic calculus is not required, and this material should be accessible to anyone familiar with elementary probability theory and linear algebra. The basic idea of pricing by arbitrage (or, rather, by non-arbitrage) is presented in Chapter 1. The unique price for a European option in a single-period binomial model is given and then extended to multi-period binomial models. Chapter 2 introduces the idea of a martingale measure for price processes. Following a discussion of the use of self-?nancing tr- ing strategies to hedge against trading risk, it is shown how options can be priced using an equivalent measure for which the discounted price p- cess is a martingale.

English | 2014 | pages: 600 | ISBN: 0071822526 | PDF | 9,5 mb

English | PDF,EPUB | 2016 | 288 Pages | ISBN : 3319307150 | 12.05 MB

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads.