Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs
نویسندگان
چکیده
منابع مشابه
Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs
Authors are encouraged to submit new papers to INFORMS journals by means of a style file template, which includes the journal title. However, use of a template does not certify that the paper has been accepted for publication in the named journal. INFORMS journal templates are for the exclusive purpose of submitting to an INFORMS journal and should not be used to distribute the papers in print ...
متن کاملBatch Bayesian Optimization via Simulation Matching
Bayesian optimization methods are often used to optimize unknown functions that are costly to evaluate. Typically, these methods sequentially select inputs to be evaluated one at a time based on a posterior over the unknown function that is updated after each evaluation. In many applications, however, it is desirable to perform multiple evaluations in parallel, which requires selecting batches ...
متن کاملBayesian CART – Prior Specification and Posterior Simulation –
We present advances in Bayesian modeling and computation for CART (classification and regression tree) models. The modeling innovations include a formal prior distributional structure for tree generation – the pinball prior – that allows for the combination of an explicit specification of a distribution for both the tree size and the tree shape. The core computational innovations involve a nove...
متن کاملBayesian Fuzzy Hypothesis Testing with Imprecise Prior Distribution
This paper considers the testing of fuzzy hypotheses on the basis of a Bayesian approach. For this, using a notion of prior distribution with interval or fuzzy-valued parameters, we extend a concept of posterior probability of a fuzzy hypothesis. Some of its properties are also put into investigation. The feasibility and effectiveness of the proposed methods are also cla...
متن کاملPerfect simulation for Bayesian wavelet thresholding with correlated coefficients
We introduce a new method of Bayesian wavelet shrinkage for reconstructing a signal when we observe a noisy version. Rather than making the usual assumption that the wavelet coefficients of the signal are independent, we allow for the possibility that they are locally correlated in both location (time) and scale (frequency). This leads us to a prior structure which is, unfortunately, analytical...
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: Operations Research
سال: 2016
ISSN: 0030-364X,1526-5463
DOI: 10.1287/opre.2016.1480