Bayesian Statistics for Beginners
a step-by-step approach
-
10% KEDVEZMÉNY?
- A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
- Kiadói listaár GBP 112.50
-
53 746 Ft (51 187 Ft + 5% áfa)
Az ár azért becsült, mert a rendelés pillanatában nem lehet pontosan tudni, hogy a beérkezéskor milyen lesz a forint árfolyama az adott termék eredeti devizájához képest. Ha a forint romlana, kissé többet, ha javulna, kissé kevesebbet kell majd fizetnie.
- Kedvezmény(ek) 10% (cc. 5 375 Ft off)
- Kedvezményes ár 48 372 Ft (46 068 Ft + 5% áfa)
Iratkozzon fel most és részesüljön kedvezőbb árainkból!
Feliratkozom
53 746 Ft
Beszerezhetőség
Megrendelésre a kiadó utánnyomja a könyvet. Rendelhető, de a szokásosnál kicsit lassabban érkezik meg.
Why don't you give exact delivery time?
A beszerzés időigényét az eddigi tapasztalatokra alapozva adjuk meg. Azért becsült, mert a terméket külföldről hozzuk be, így a kiadó kiszolgálásának pillanatnyi gyorsaságától is függ. A megadottnál gyorsabb és lassabb szállítás is elképzelhető, de mindent megteszünk, hogy Ön a lehető leghamarabb jusson hozzá a termékhez.
A termék adatai:
- Kiadó OUP Oxford
- Megjelenés dátuma 2019. május 29.
- ISBN 9780198841296
- Kötéstípus Keménykötés
- Terjedelem430 oldal
- Méret 247x193x26 mm
- Súly 1038 g
- Nyelv angol 0
Kategóriák
Rövid leírás:
This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.
TöbbHosszú leírás:
Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources.
Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields.
While reading this book, I joined the authors on a learning endeavor thanks to their honesty and intellectual vulnerability. Their lack of experience with Bayesian statistics helps them to be effective communicators . . . If you are interested in starting your Bayesian journey, then Bayesian Statistics for Beginners is an excellent place to begin.
Tartalomjegyzék:
Section 1
Basics of Probability
Introduction to Probability
Joint, Marginal, and Conditional Probability
Section 2
Bayes' Theorem and Bayesian Inference
Bayes' Theorem
Bayesian Inference
The Author Problem - Bayesian Inference with Two Hypotheses
The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses
The Portrait Problem: Bayesian Inference with Joint Likelihood
Section 3
Probability Functions
Probability Mass Functions
Probability Density Functions
Section 4
Bayesian Conjugates
The White House Problem: The Beta-Binomial Conjugate
The Shark Attack Problem: The Gamma-Poisson Conjugate
The Maple Syrup Problem: The Normal-Normal Conjugate
Section 5
Markov Chain Monte Carlo
The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm
MCMC Diagnostic Approaches
The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm
The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling
Section 6
Applications
The Survivor Problem: Simple Linear Regression with MCMC
The Survivor Problem Continued: Introduction to Bayesian Model Selection
The Lorax Problem: Introduction to Bayesian Networks
The Once-ler Problem: Introduction to Decision Trees
Appendices
Appendix 1: The Beta-Binomial Conjugate Solution
Appendix 2: The Gamma-Poisson Conjugate Solution
Appendix 3: The Normal-Normal Conjugate Solution
Appendix 4: Conjugate Solutions for Simple Linear Regression
Appendix 5: The Standardization of Regression Data
