Cost-Optimized Reliability Test Planning and Decision-Making Through Bayesian Methods and Leveraging Prior Knowledge

Charles Recchia
Charles H Recchia, MBA, PhD has more than twenty-five years of product development, technical team management, and fundamental research experience with a special focus on reliability statistics of complex systems. He earned his doctorate in Solid-State Physics from The Ohio State University, and a Master of Business Administration degree from Babson College. Dr. Recchia acquired in-depth reliabili...read more



Details

When planning for and interpreting reliability datasets proper application of Bayesian statistics leads to improved decision-making, resource utilization and allows for rigorous treatment of prior knowledge to optimize overall reliability program costs and increase return on investment. In this webinar, we build upon the foundation established in our previous intro-level presentation and provide specific examples of reduced sample sizes enabled by Bayesian methods. We also describe real-world scenarios of improved decision-making during comparative reliability analyses using proper statistical perspectives on relative failure rates between systems.

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  • How to conduct an effective reliability growth program (如何开展有效的可靠性增长项目)
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