<p><p>This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost a
Advanced Data Analysis in Neuroscience. Integrating Statistical and Computational Models
β Scribed by Daniel Durstewitz
- Publisher
- Springer
- Year
- 2017
- Tongue
- English
- Leaves
- 304
- Category
- Library
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
<p><p>This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas suc
Modern hydrology is more interdisciplinary than ever. Staggering amounts and varieties of information pour in from GIS and remote sensing systems every day, and this information must be collected, interpreted, and shared efficiently. Hydroinformatics: Data Integrative Approaches in Computation, Anal
<p>The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developm
<p><p>This book is a tribute to Professor Jacek Ε»urada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Ε»urada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Compu
Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscien