Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues
|
Buy Now
Bayesian Inference and Maximum Entropy Methods in Science and Engineering - MaxEnt 37, Jarinu, Brazil, July 09-14, 2017 (Hardcover, 1st ed. 2018)
Loot Price: R5,173
Discovery Miles 51 730
|
|
Bayesian Inference and Maximum Entropy Methods in Science and Engineering - MaxEnt 37, Jarinu, Brazil, July 09-14, 2017 (Hardcover, 1st ed. 2018)
Series: Springer Proceedings in Mathematics & Statistics, 239
Expected to ship within 12 - 17 working days
|
These proceedings from the 37th International Workshop on Bayesian
Inference and Maximum Entropy Methods in Science and Engineering
(MaxEnt 2017), held in Sao Carlos, Brazil, aim to expand the
available research on Bayesian methods and promote their
application in the scientific community. They gather research from
scholars in many different fields who use inductive statistics
methods and focus on the foundations of the Bayesian paradigm,
their comparison to objectivistic or frequentist statistics
counterparts, and their appropriate applications. Interest in the
foundations of inductive statistics has been growing with the
increasing availability of Bayesian methodological alternatives,
and scientists now face much more difficult choices in finding the
optimal methods to apply to their problems. By carefully examining
and discussing the relevant foundations, the scientific community
can avoid applying Bayesian methods on a merely ad hoc basis. For
over 35 years, the MaxEnt workshops have explored the use of
Bayesian and Maximum Entropy methods in scientific and engineering
application contexts. The workshops welcome contributions on all
aspects of probabilistic inference, including novel techniques and
applications, and work that sheds new light on the foundations of
inference. Areas of application in these workshops include
astronomy and astrophysics, chemistry, communications theory,
cosmology, climate studies, earth science, fluid mechanics,
genetics, geophysics, machine learning, materials science, medical
imaging, nanoscience, source separation, thermodynamics
(equilibrium and non-equilibrium), particle physics, plasma
physics, quantum mechanics, robotics, and the social sciences.
Bayesian computational techniques such as Markov chain Monte Carlo
sampling are also regular topics, as are approximate inferential
methods. Foundational issues involving probability theory and
information theory, as well as novel applications of inference to
illuminate the foundations of physical theories, are also of keen
interest.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
You might also like..
|