Parameter estimation via conditional expectation: a Bayesian inversion
نویسندگان
چکیده
منابع مشابه
Parameter estimation via conditional expectation: a Bayesian inversion
When a mathematical or computational model is used to analyse some system, it is usual that some parameters resp. functions or fields in the model are not known, and hence uncertain. These parametric quantities are then identified by actual observations of the response of the real system. In a probabilistic setting, Bayes’s theory is the proper mathematical background for this identification pr...
متن کاملBayesian parameter estimation via variational methods
We consider a logistic regression model with a Gaussian prior distribution over the parameters. We show that an accurate variational transformation can be used to obtain a closed form approximation to the posterior distribution of the parameters thereby yielding an approximate posterior predictive model. This approach is readily extended to binary graphical model with complete observations. For...
متن کاملImplicit parameter estimation for conditional Gaussian Bayesian networks
The Bayesian estimation of the conditional Gaussian parameter needs to define several a priori parameters. The proposed approach is free from this definition of priors. We use the Implicit estimation method for learning from observations without a prior knowledge. We illustrate the interest of such an estimation method by giving first the Bayesian Expectation A Posteriori estimator for conditio...
متن کاملConditional expectation estimation through attributable components
A general methodology is proposed for the explanation of variability in a quantity of interest x in terms of covariates z = (z1, . . . ,zL). It provides the conditional mean x̄(z) as a sum of components, where each component is represented as a product of non-parametric one-dimensional functions of each covariate zl that are computed through an alternating projection procedure. Both x and the zl...
متن کاملDerandomizing via the Method of Conditional Expectation
Suppose we want to derandomize A—that is, give a deterministic variant of A which succeeds with probabilty 1 on every input. Sometimes we can do this using the method of conditional expectation. We can think of A as a binary tree which, given x, branches on the sampled value of each random bit Ri in turn. Paths in this tree correspond to different possible random strings R1, . . . , Rm that cou...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advanced Modeling and Simulation in Engineering Sciences
سال: 2016
ISSN: 2213-7467
DOI: 10.1186/s40323-016-0075-7