By Patrick T. Tam
A Physicists advisor to Mathematica(r) teaches scholars physicists easy methods to grasp Mathematica utilizing examples and methods that would attract them. The booklet illustrates the usefulness of Mathematica in studying, instructing, and conducting learn in physics. half One provides a realistic, physics-oriented, and self-contained advent to this system. half covers the appliance of Mathematica to mechanics, electricityand magnetism, and quantum physics.Mathematica allows the consumer to unravel quite a lot of physics difficulties, from crucial to those who are only for enjoyable, and offers an atmosphere that permits the person to enhance a better intuitive figuring out of physics. This e-book aids the reader in utilizing Mathematica for numerical, symbolic, and graphical calculations, and in addition demonstrates the courses power to animate - and three-d pix. Tams therapy of the topic is vastly particular, and makes this ebook a vital reference for somebody wanting an creation to Mathematicas software to physics.This e-book teaches upper-division and graduate physics scholars in addition to specialist physicists the way to grasp Mathematic(r), utilizing examples and methods which are motivating to them. It additionally exhibits that this desktop algebra process could be a strong and beautiful instrument for studying, instructing, and doing physics. half I supplies a realistic, physics-oriented, and self-contained advent to Mathematica. half II considers the applying of Mathematica to mechanics, electrical energy and magnetism, and quantum physics. * calls for no past wisdom of Mathematica or desktop programming* incorporates a disk containing all Mathematica enter utilized in the textual content for useful software, which might be utilized by either Macintosh and home windows clients
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Extra info for A Physicist's Guide to Mathematica
2. Two-part code interpretation For large samples, the stochastic complexity can be interpreted as a two-part codelength of the data after all, where hypotheses H are encoded with a special code that works by ﬁrst discretizing the model space H into a set of “maximally distinguishable hypotheses,” and then assigning equal codelength to each of these. 3. Bayesian interpretation In many cases, reﬁned MDL model selection coincides with Bayes factor model selection based on a noninformative prior such as Jeffreys’ prior (Bernardo and Smith 1994).
Both models have two free parameters; nevertheless, according to the reﬁned version of MDL model selection to be introduced in Part III, Chapter 14 of this book, Stevens’s model is in a sense “more complex” than Fechner’s (see page 417). Roughly speaking, this means there are a lot more data patterns that can be explained by Stevens’s model than can be explained by Fechner’s model. Somewhat more precisely, the number of data patterns (sequences of data) of a given length that can be ﬁt well by Stevens’s model is much larger than the number of data patterns of the same length that can be ﬁt well by Fechner’s model.
Combining the idea of designing codes so as to achieve essentially minimax optimal codelengths with the combined use of one-part and twopart codes (whichever is appropriate for the situation at hand) has culminated in a theory of inductive inference that we call reﬁned MDL. We discuss it in more detail in the next subsection. Crude Two-Part MDL (Part I, Chapter 5 of this book) In this book, we use the term “crude MDL” to refer to applications of MDL for model and hypothesis selection of the type described in the box on page 14, as long as the hypotheses H ∈ H are encoded in “intuitively reasonable” but ad-hoc ways.
A Physicist's Guide to Mathematica by Patrick T. Tam