Patrick Muldowney - Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics
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- Название:Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics
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Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics: краткое содержание, описание и аннотация
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, left off,
introduces readers to particular problems of integration in the probability-like theory of quantum mechanics. Written as a motivational explanation of the key points of the underlying mathematical theory, and including ample illustrations of the calculus, this book relies heavily on the mathematical theory set out in the author’s previous work. That said, this work stands alone and does not require a reading of
in order to be understandable.
Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics Stochastic calculus, including discussions of random variation, integration and probability, and stochastic processes. Field theory, including discussions of gauges for product spaces and quantum electrodynamics. Robust and thorough appendices, examples, illustrations, and introductions for each of the concepts discussed within. An introduction to basic gauge integral theory. The methods employed in this book show, for instance, that it is no longer necessary to resort to unreliable «Black Box» theory in financial calculus; that full mathematical rigor can now be combined with clarity and simplicity. Perfect for students and academics with even a passing interest in the application of the gauge integral technique pioneered by R. Henstock and J. Kurzweil,
is an illuminating and insightful exploration of the complex mathematical topics contained within.
was the family of all subsets of
, and the example was illustrated by means of two distinct probability measures
, one of which was based on Up and Down transitions being equally likely, where for the other measure an Up transition was twice as likely as a Down.
for
; so the elements
of
consist of sixteen 4‐tuples of the form
be the family of all subsets
of
; so
contains
members, one of which (for example) is
consisting of five individual four‐tuples. Assume that Up transitions and Down transitions are equally likely, and that they are independent events. Then, as before,
. For
above, 
, and let
denote the stochastic integrals of the preceding section, so for
,
gives the values
of Table 2.4. As described in Section 2.3, the rationale for deducing the probabilities of outcomes
,
, from the probabilities on
is the relationship
and
) must be measurable. But that is no problem in this case since all the sets involved are finite. To illustrate the calculation, take
. Then, referring to Table 2.4whenever necessary,