0.0 z 5 hvězdiček
měkká vazba
Bayesian inference with INLA
Kniha ( měkká vazba )
- Produkt je nedostupný.
The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence… Přejít na celý popis
Extrémy vzrušují! Nestyďte se přiznat, že jste zvědaví. Dark romance, která rozdělila internet je tady!.
Více informací
K tomuto produktu zákazníci kupují
-
The Night Ends With Fire
-
A Dance With Dragons, part 1: Dreams and Dust
-
The Neil Gaiman Collection
-
Alice´s Adventures in Wonderland: Unabridged, with Poems, Letters & Biography
-
Trouble with Lichen
-
A Court of Thorns and Roses Collector´s Edition
-
In A Glass Darkly
-
NOVINKA
Final Fantasy Vii Remake: Material Ultimania Plus
-
The Battle of Maldon - together with The Homecoming of Beorhtnoth
-
Harry Potter and the Chamber of Secrets: MinaLima Edition
-
A Dance with Dragons, part 1: Dreams and Dust
-
Second Great Dune Trilogy (Emperor, Heretics, Chapterhouse)
-
Anansi Boys
-
DUNE: The Graphic Novel, Book 1: Dune
-
The Macmillan Collection of Myths and Legends
-
Folk and Fairy Tales of Azeroth
-
The Complete Guide to Middle-earth: The Definitive Guide to the World of J.R.R. Tolkien
-
A Dance with Dragons, part 1: Dreams and Dust
-
Harry Potter and the Order of the Phoenix
-
The History of the Hobbit
-
Zodiac Academy 7: Heartless Sky
-
Merciless Saviors
-
A Dance With Dragons, part 2: After the Feast
-
The History of Middle-Earth 04: Shaping of Middle-Earth
The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed.
Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website.
This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.
- Nakladatel
- Taylor & Francis
- Rozměr
- 178 x 254 x 22
- jazyk
- angličtina
- Počet stran
- 332
- Vydání
- 1
- isbn
- 978-1-03-217453-2
- Vazba
- měkká vazba
- datum vydání
- 1.09.2021
- ean
- 9781032174532
Hodnocení a recenze čtenářů Nápověda
0.0 z 5 0 hodnocení čtenářů
0× 5 hvězdiček 0× 4 hvězdičky 0× 3 hvězdičky 0× 2 hvězdičky 0× 1 hvezdička
Přidejte své hodnocení knihy
Vývoj ceny Nápověda
Získejte přehled o vývoji ceny za posledních 60 dní.
Maloobchodní cena
Minimální prodejní cena:
0 Kč
Nápověda
Články, které stojí za pozornost
-
-
-
-
-
-
-
-
-
-
-
-
