Non Parametric Fitting

Computer Vision-Become an ace of Computer Vision, Computer Vision for Apps using Python, OpenCV, TensorFlow, etc.

by Masashi Sugiyama (Author), Taiji Suzuki (Author), Takafumi Kanamori (Author)

A rigorous and engaging deep-dive into statistics and machine-learning, with hands-on applications in Python and MATLAB.

to excel in Data Science and Machine Learning

Presents new and up-dated material on both the underlying theory and the practical methodology of directional statistics, helping the reader to utilise and develop the techniques appropriate to their work.
The book is divided into three parts. The first part concentrates on statistics on the circle. Topics covered include tests of uniformity, tests of good-of-fit, inference on von Mises distributions and non-parametric methods. The second part considers statistics on spheres of arbitrary dimension, and includes a detailed account of inference on the main distributions on spheres. Recent material on correlation, regression time series, robust techniques, bootstrap methods, density estimation and curve fitting is presented. The third part considers statistics on more general sample spaces, in particular rotation groups, Stiefel manifolds, Grassmann manifolds and complex projective spaces. Shape analysis is considered from the perspective of directional statistics.