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1
Bayesian Inference with INLA
Chapman & Hall/CRC
Virgilio Gómez-Rubio
inla
effects
models
random
precision
posterior
function
effect
values
prior
gaussian
latent
observations
dataset
linear
summary
likelihood
marginal
matrix
parameters
figure
hyperparameters
fixed
0.025quant
0.5quant
0.975quant
compute
analysis
bayesian
iid
defined
estimates
package
computed
predictor
obtained
formula
intercept
priors
parameter
survival
statistical
variables
fitted
expected
poisson
statistics
total
3rd
covariates
Anno:
2020
Lingua:
english
File:
PDF, 14.91 MB
I tuoi tag:
0
/
5.0
english, 2020
2
Bayesian Regression Modeling with Inla
CRC Press
Faraway
,
Julian James
,
Wang
,
Xiaofeng
,
Yue
,
Yu
inla
regression
models
posterior
function
bayesian
prior
linear
gaussian
random
figure
modeling
variables
density
error
effect
values
analysis
exp
formula
parameter
priors
response
intercept
interval
parameters
effects
precision
credible
zero
matrix
estimate
probability
observations
fixed
method
survival
compute
smoothing
generalized
0.025quant
0.975quant
likelihood
predictor
approach
methods
0.5quant
package
poisson
splines
Anno:
2018
Lingua:
english
File:
PDF, 19.43 MB
I tuoi tag:
0
/
0
english, 2018
3
Bayesian Regression Modeling with INLA
CRC
Xiaofeng Wang
,
Yu Ryan Yue
,
Julian Faraway
inla
regression
models
posterior
function
bayesian
prior
linear
gaussian
random
figure
modeling
variables
density
error
effect
values
exp
formula
parameter
analysis
priors
response
intercept
interval
parameters
effects
precision
credible
zero
matrix
estimate
probability
observations
fixed
method
survival
compute
smoothing
generalized
0.025quant
0.975quant
likelihood
predictor
methods
approach
0.5quant
package
poisson
splines
Anno:
2018
Lingua:
english
File:
PDF, 17.53 MB
I tuoi tag:
0
/
0
english, 2018
4
Advanced spatial modeling with stochastic partial differential equations using R and INLA
CRC Press
Krainski
,
Elias T
mesh
inla
function
posterior
range
matrix
values
gaussian
precision
figure
random
effects
spde
models
locations
parameters
effect
observations
prior
linear
defined
marginal
likelihood
standard
parameter
intercept
package
fixed
density
summary
simulated
deviation
fitted
stack
hyperparameters
modeling
predictor
covariate
dataset
inla.stack
considering
temporal
theta
response
compute
fitting
nodes
computed
covariance
noise
Anno:
2019
Lingua:
english
File:
PDF, 40.84 MB
I tuoi tag:
0
/
0
english, 2019
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