Listing 1 - 10 of 306 | << page >> |
Sort by
|
Choose an application
Probability theory is a rapidly expanding field and is used in many areas of science and technology. Beginning from a basis of abstract analysis, this mathematics book develops the knowledge needed for advanced students to develop a complex understanding of probability. The first part of the book systematically presents concepts and results from analysis before embarking on the study of probability theory. The initial section will also be useful for those interested in topology, measure theory, real analysis and functional analysis. The second part of the book presents the concepts, methodology.
Choose an application
Jason Gibson explains how to calculate frequency distributions. He explains how these calculations would be useful, then provides examples to increase understanding.
Choose an application
Jason Gibson continues examining Z-score calculations in a second segment. Using visual representations, charts and reassuring technique, he guides maths students toward an understanding of statistics.
Choose an application
In part three of his discussion on Z-values, instructor Jason Gibson takes on a statistical problem with a dollar value. He demonstrates how to apply approaches from previous lessons and acquire a range for a percentage.
Choose an application
In his fourth and final segment on z-values, Jason Gibson solves a new problem while reviewing methods. He also emphasizes the importance of understanding normal distribution.
Choose an application
Instructor Jason Gibson discusses Z-value calculation and uses tips, shortcuts and technique to guide students through difficulties. His demonstration tackles challenge areas and shows when shortcuts can help approximate answers.
Choose an application
Intended for class use or self-study, the second addition of this text aspires like the first to introduce statistical methodology to a wide audience, simply and intuitively, through resampling from the data at hand. The methodology proceeds from chapter to chapter from the simple to the complex.
Resampling (Statistics) --- Mathematics --- Probability --- Statistics
Choose an application
The Gibbs measure is a probability measure, which has been an important object in many problems of probability theory and statistical mechanics. It is the measure associated with the Hamiltonian of a physical system (a model) and generalizes the notion of a canonical ensemble. More importantly, when the Hamiltonian can be written as a sum of parts, the Gibbs measure has the Markov property (a certain kind of statistical independence), thus leading to its widespread appearance in many problems outside of physics such as biology, Hopfield networks, Markov networks, and Markov logic networks. Mor
Probability measures. --- Distribution (Probability theory) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Measures, Normalized --- Measures, Probability --- Normalized measures
Choose an application
"If the number of sample observations n ! 1, the statistic in (1.1) will follow the chi-squared probability distribution with r-1 degrees of freedom. We know that this remarkable result is true only for a simple null hypothesis when a hypothetical distribution is specified uniquely (i.e., the parameter is considered to be known). Until 1934, Pearson believed that the limiting distribution of the statistic in (1.1) will be the same if the unknown parameters of the null hypothesis are replaced by their estimates based on a sample; see, for example, Baird (1983), Plackett (1983, p. 63), Lindley (1996), Rao (2002), and Stigler (2008, p. 266). In this regard, it is important to reproduce the words of Plackett (1983, p. 69) concerning E. S. Pearson's opinion: "I knew long ago that KP (meaning Karl Pearson) used the 'correct' degrees of freedom for (a) difference between two samples and (b) multiple contingency tables. But he could not see that in curve fitting should be got asymptotically into the same category." Plackett explained that this crucial mistake of Pearson arose from to Karl Pearson's assumption "that individual normality implies joint normality." Stigler (2008) noted that this error of Pearson "has left a positive and lasting negative impression upon the statistical world." Fisher (1924) clearly showed 1 2 CHAPTER 1. A HISTORICAL ACCOUNT that the number of degrees of freedom of Pearson's test must be reduced by the number of parameters estimated from the sample"--
Choose an application
Seit seinem Erscheinen hat sich das Buch umgehend als Standardwerk für eine umfassende und moderne Einführung in die Wahrscheinlichkeitstheorie und ihre maßtheoretischen Grundlagen etabliert. Themenschwerpunkte sind: Maß- und Integrationstheorie, Grenzwertsätze für Summen von Zufallsvariablen (Gesetze der Großen Zahl, Zentraler Grenzwertsatz, Ergodensätze, Gesetz vom iterierten Logarithmus, Invarianzprinzipien, unbegrenzt teilbare Verteilungen), Martingale, Perkolation, Markovketten und elektrische Netzwerke, Konstruktion stochastischer Prozesse, Poisson'scher Punktprozess, Brown'sche Bewegung, stochastisches Integral und stochastische Differentialgleichungen. Bei der Bearbeitung der Neuauflage wurde viel Wert auf eine noch zugänglichere didaktische Aufbereitung des Textes gelegt, und es wurden viele neue Abbildungen sowie Textergänzungen hinzugefügt.
Listing 1 - 10 of 306 | << page >> |
Sort by
|