Teknik Informatika S1

Foundations of Predictive Analytics

---

In this book we cover the essentials of the foundations of statistical modeling.
We begin with the concepts and properties of statistical distributions,
and describe important properties of the various frequently encountered distributions
as well as some of the more exotic but useful ones. We have an
extensive section on matrix properties that are fundamental to many of the
algorithmic techniques we use in modeling. This includes sections on unusual
yet useful methods to split complex calculations and to iterate with the inclusion/exclusion
of specic data points. We describe the characteristics of the
most common modeling frameworks, both linear and nonlinear. Time series
or forecasting models have certain idiosyncrasies that are suciently special
so we devote a chapter to these methods.
There is description of how and why to prepare the data for statistical
modeling, including much discussion on practical approaches. We discuss the
variety of goodness measures and how and when they are applied. There
is a section on optimization methods which are required for many of the
training algorithms. We also have description on some of the less well-known
topics such as multidimensional scaling, Dempster{Shafer Theory, and some
practical discussion about simulation and reject inference.

---

Ketersediaan

Tidak ada salinan data

Informasi Detail

Judul Seri

-

No. Panggil

-

Penerbit

United States of Amerika : CRC press LLC., 2012

Deskripsi Fisik

-

Bahasa

English

ISBN/ISSN

978-1-4398-6948-2

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

-

Info Detail Spesifik

335 hlm

Pernyataan Tanggungjawab

James Wu

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar