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The problem is to determine an output feedback law that is optimal in the sense of minimizing the expected value of a quadratic cost criterion. Output measurements are assumed to be corrupted by Gaussian noise and the initial state, likewise, is assumed to be a Gaussian random vector.
In process improvement efforts, quality costs tite or cost of quality (sometimes abbreviated CoQ or COQ [1]) is a means to quantify the total cost of quality-related efforts and deficiencies. It was first described by Armand V. Feigenbaum in a 1956 Harvard Business Review article.
Data on medical costs are often combined with QALYs in cost-utility analysis to estimate the cost-per-QALY associated with a health care intervention. This parameter can be used to develop a cost-effectiveness analysis of any treatment.
Moving horizon estimation uses a sliding time window. At each sampling time the window moves one step forward. It estimates the states in the window by analyzing the measured output sequence and uses the last estimated state out of the window, as the prior knowledge.
As such, the ICER facilitates comparison of interventions across various disease states and treatments. In 2009, NICE set the nominal cost-per-QALY threshold at £50,000 for end-of-life care because dying patients typically benefit from any treatment for a matter of months, making the treatment's QALYs small. [3]
Thus, any health intervention which has an incremental cost of more than £30,000 per additional QALY gained is likely to be rejected and any intervention which has an incremental cost of less than or equal to £30,000 per extra QALY gained is likely to be accepted as cost-effective. This implies a value of a full life of about £2.4 million.
Cost of poor quality (COPQ) or poor quality costs (PQC) or cost of nonquality, are costs that would disappear if systems, processes, and products were perfect. COPQ was popularized by IBM quality expert H. James Harrington in his 1987 book Poor-Quality Cost. [1] COPQ is a refinement of the concept of quality costs.
The antithetic variates technique consists, for every sample path obtained, in taking its antithetic path — that is given a path {, …,} to also take {, …,}.The advantage of this technique is twofold: it reduces the number of normal samples to be taken to generate N paths, and it reduces the variance of the sample paths, improving the precision.
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