Marginal Inferential Models: Prior-Free Probabilistic Inference on Interest Parameters
نویسندگان
چکیده
منابع مشابه
Marginal inferential models
Inferential models (IMs) provide a general framework for prior-free, frequencycalibrated, posterior probabilistic inference. The fundamental idea is the use of unobservable auxiliary variables to describe the underlying uncertainty about the parameter of interest. When nuisance parameters are present, a marginalization step can reduce the dimension of the auxiliary variable, which in turn leads...
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This paper is a continuation of the authors’ theoretical investigation of inferential model (IMs); see Martin, Hwang and Liu (2010). The fundamental idea is that prior-free posterior probability-like inference with desirable long-run frequency properties can be achieved through a system based on predicting unobserved auxiliary variables. In Part I, an intermediate conditioning step was proposed...
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While most current work in POMDP planning focus on the development of scalable approximate algorithms, existing techniques often neglect performance guarantees and sacrifice solution quality to improve efficiency. In contrast, our approach to optimizing POMDP controllers by probabilistic inference and obtaining bounded on solution quality can be summarized as follows: (1) re-formulate POMDP pla...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2015
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2014.985827