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Possible Answers:
INLA.
Last seen on: Wall Street Journal Crossword – December 06 2022 – Book Fare
Random information on the term “INLA”:
Integrated nested Laplace approximations (INLA) is a method for approximate Bayesian inference based on Laplace’s method. It is designed for a class of models called latent Gaussian models (LGMs), for which it can be a fast and accurate alternative for Markov chain Monte Carlo methods to compute posterior marginal distributions. Due to its relative speed even with large data sets for certain problems and models, INLA has been a popular inference method in applied statistics, in particular spatial statistics, ecology, and epidemiology. It is also possible to combine INLA with a finite element method solution of a stochastic partial differential equation to study e.g. spatial point processes and species distribution models. The INLA method is implemented in the R-INLA R package.
Let y = ( y 1 , … , y n ) {\displaystyle {\boldsymbol {y}}=(y_{1},\dots ,y_{n})} denote the response variable (that is, the observations) which belongs to an exponential family, with the mean μ i {\displaystyle \mu _{i}} (of y i {\displaystyle y_{i}} ) being linked to a linear predictor η i {\displaystyle \eta _{i}} via an appropriate link function. The linear predictor can take the form of a (Bayesian) additive model. All latent effects (the linear predictor, the intercept, coefficients of possible covariates, and so on) are collectively denoted by the vector x {\displaystyle {\boldsymbol {x}}} . The hyperparameters of the model are denoted by θ {\displaystyle {\boldsymbol {\theta }}} . As per Bayesian statistics, x {\displaystyle {\boldsymbol {x}}} and θ {\displaystyle {\boldsymbol {\theta }}} are random variables with prior distributions.