Above are papers related to the following paper: [Paper_ID] Paper #49753. 	[Title] Bayesian Design for the Normal Linear Model with Unknown Error Variance [Abstract] Most of the Bayesian theory of optimal experimental design, for the normal linear model, has been developed under the restrictive assumption that the variance is known. In special cases, insensitivity of specific design criteria to specific prior assumptions on the variance has been demonstrated, but a general result to show the way in which Bayesian optimal designs are affected by prior information about the variance is lacking. This paper stresses the important distinction between expected utility functions and optimality criteria, examines a number of expected utility functions some of which possess interesting properties, and deserve wider use and derives the relevant Bayesian optimality criteria under normal assumptions. This unifying setup is useful for proving the main result of the paper, that clarifies the issue of designing for the normal linear model with unknown variance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49720. 	[Title] Bayesian Experimental Design: A Review [Abstract] Non Bayesian experimental design for linear models has been reviewed by Stein-berg and Hunter (1984) and in the recent book by Pukelsheim (1993); Ford, Kitsos and Titterington (1989) reviewed non Bayesian design for nonlinear models. Bayesian design for both linear and nonlinear models is reviewed here. We argue that the design problem is best considered as a decision problem and that it is best solved by maximizing the expected utility of the experiment. This paper considers only in a marginal way, when appropriate, the theory of non Bayesian design. Please summarise all the given information.

Paper #49753 has the title `Bayesian Design for the Normal Linear Model with Unknown Error Variance  
` and the abstract `Most of the Bayesian theory of optimal experimental design, for the normal linear model, has been developed under the restrictive assumption that the variance is known. In special cases, insensitivity of specific design criteria to specific prior assumptions on the variance has been demonstrated, but a general result to show the way in which Bayesian optimal designs are affected by prior information about the variance is lacking. This paper stresses the important distinction between expected utility functions and optimality criteria, examines a number of expected utility functions some of which possess interesting properties, and deserve wider use and derives the relevant Bayesian optimality criteria under normal assumptions. This unifying setup is useful for proving the main result of the paper, that clarifies the issue of designing for the normal linear model with unknown variance. 
`.
Question: Which category should Paper #49753 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #49753 has the title `Bayesian Design for the Normal Linear Model with Unknown Error Variance  
` and the abstract `Most of the Bayesian theory of optimal experimental design, for the normal linear model, has been developed under the restrictive assumption that the variance is known. In special cases, insensitivity of specific design criteria to specific prior assumptions on the variance has been demonstrated, but a general result to show the way in which Bayesian optimal designs are affected by prior information about the variance is lacking. This paper stresses the important distinction between expected utility functions and optimality criteria, examines a number of expected utility functions some of which possess interesting properties, and deserve wider use and derives the relevant Bayesian optimality criteria under normal assumptions. This unifying setup is useful for proving the main result of the paper, that clarifies the issue of designing for the normal linear model with unknown variance. 
`.
Question: Which category should Paper #49753 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods