Bayesian statistics draws inferences from data in a way that makes sense to ordinary humans. It directly addresses the questions people naturally ask, rather than inverting the questions for technical convenience.
Bayesian inference allows you to bring together all sources of information, subjective and objective. You can combine expert opinion and intuition with data, weighing each in the proportion appropriate for your situation. The weight given different kinds of information automatically adjusts according to the quantity and quality of each.
Because of this ability to adjust to new information, Bayesian methods naturally lend themselves to adaptive decision making, updating the representation of your knowledge of the world as new data become available.
For over a decade I applied Bayesian methods to cancer treatment, using these methods to design, simulate, and conduct adaptive clinical trials at MD Anderson Cancer Center in Houston. Now I am applying these methods to business opportunities. I have extensive experience in Bayesian analysis and especially in the computational techniques necessary to make Bayesian methods practical.
|“John is always a pleasure to work with. He can solve your mathematical problems, your computing problems, and is always eager to help with particularly challenging research projects.” — Luis Pericchi, University of Puerto Rico|
Below are a few of my publications in Bayesian statistics. A full list of publications and abstracts is available here.
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