Posted by **Grev27** at March 30, 2018

English | 2 Oct. 2015 | ISBN: 0133902838 | 250 Pages | PDF (True) | 19.4 MB

Posted by **AvaxGenius** at Dec. 2, 2017

English | PDF | 2017 | 108 Pages | ISBN : 3658201665 | 3.17 MB

Sabine Stübler compares different proteasome isoforms and subtypes in terms of their transport and active site-related parameters applying an existing computational model. In a second step, the author extends this model to be able to describe the influence of proteasome inhibitors in in vitro experiments. The computational model, which describes the hydrolysis of short fluorogenic peptides by the 20S proteasome, is calibrated to experimental data from different proteasome isoforms using an approximate Bayesian computation approach. The dynamics of proteasome inhibitors are included into the model in order to demonstrate how to modulate the inhibitor’s transport parameters for strong or isoform-specific inhibition.

Posted by **Jeembo** at May 21, 2018

English | 2017 | ISBN: 3319542737 | 275 Pages | PDF | 13.0 MB

In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters.

Posted by **arundhati** at May 10, 2018

2018 | ISBN-10: 1482238063 | 330 pages | PDF | 6 MB

Posted by **exLib** at May 2, 2018

ITExLi | 2018 | ISBN: 1789230934 9781789230932 1789230926 9781789230925 | 131 pages | PDF | 11 MB

This book is an introduction to the mathematical analysis of Bayesian decision-making when the state of the problem is unknown but further data about it can be obtained.

Posted by **DZ123** at Feb. 4, 2018

English | 2005 | ISBN: 0471679321 | PDF | pages: 323 | 3.0 mb

Posted by **Grev27** at Jan. 21, 2018

English | ISBN: 1785883801 | 2016 | EPUB/MOBI+Code files | 282 pages | 43 MB

Posted by **hill0** at Jan. 19, 2018

English | 29 Oct. 2013 | ISBN: 1461486866 | 312 Pages | EPUB | 5.34 MB

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package

Posted by **ChrisRedfield** at Nov. 21, 2017

Published: 2013-10-29 | ISBN: 1461486866, 1493950495 | PDF | 296 pages | 7.42 MB

Posted by **exLib** at Nov. 4, 2017

ITexLi | 2017 | ISBN: 9535135783 9535135775 9789535135777 9789535135784 | 356 pages | PDF | 19 MB

This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers