A fast and simple method is proposed that produces approximate multivariate local D-optimal designs of high e¢ ciency for models with binary response. The method assumes availability of a D-optimal design for a parallel normal response linear problem that has the same linear predictor, with an assumption of homogenous variance; the change required to transform the standard design into an e¢ cie...

The objective of the current study was to develop and optimize a sublingual tablet formulation of captopril which is an effective drug in the treatment of hypertension. Captopril containing tablets were prepared by direct compression method using different ingredients such as polyvinyl pyrrolidone, starch 1500, sodium starch glycolate and lactose (independent variables) and magnesium stearate, ...

This research provides an optimal design of structural parameters for ring-die granulators used in the cool briquetting process. Experimental research on the briquetting rate of pellets was carried out for three kinds of crop straws with different granularities, moisture ratios, and lengthdiameter ratios of the die hole. Results showed that: when the swoop angle β was 45 and the diameter ratio ...

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Consider a Bayesian optimal design with many support points which poses the problem of collecting data with a few number of observations at each design point. Under such a scenario the asymptotic property of using Fisher information matrix for approximating the covariance matrix of posterior ML estimators might be doubtful. We suggest to use Bhattcharyya matrix in deriving the information matri...

When optimizing an accelerated degradation testing (ADT) plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, whe...

Conventional techniques for optimal experimental design are based on local sensitivity analysis to quantify parameter effects on the output. However, one of the key challenges for experimental design is that the local sensitivity is dependent on the unknown parameter values for a nonlinear model. This problem can be addressed if the sensitivity matrix, used in experimental design, could be comp...

The use of Bayesian methodologies for solving optimal experimental design problems has increased. Many of these methods have been found to be computationally intensive for design problems that require a large number of design points. A simulation-based approach that can be used to solve optimal design problems in which one is interested in finding a large number of (near) optimal design points ...

Bayesian optimal experimental design is a sub-field of statistics focused on developing methods to make efficient use resources. Any potential evaluated in terms utility function, such as the (theoretically well-justified) expected information gain (EIG); unfortunately however, under most circumstances EIG intractable evaluate. In this work we build off successful variational approaches, which ...