Bayesian
Statistics
Classical Statistics, the stats we
all learned in Stats 101 in university, was developed to support
science. Like science, classical statistics is all about rejecting
(never accepting, even though it is often taught in stats classes and
described in many a stats textbook erroneously as "
accepting") a hypothesis. With classical statistics, we only
continue to consider a hypothesis as possibly true because we have not
found enough evidence to reject it as false. Making a decision in
engineering to use a particular technology in a design Bayesian statistics, really just a good and thorough application of probability, was however intended to assist with decision making, and that includes decision making in engineering, systems engineering, and project management. Bayesian statistics is all about determining how sure we can be that a hypothesis is actually true based on the data and information we do actually have. This is really what we need in engineering, systems engineering, and project management, not making decisions to use an alternative only because we could not reject it. In fact, all of the statistics in all of the decision theory and risk foundational textbooks has been Bayesian statistics. The question then arises as to if this is the case, why have we not been using Bayesian statistics in engineering, systems engineering, and project management for the past 70 or so years? The answer is quite simple: solutions derived using Bayesian statistics for real world problems are almost never analytically tractable nor even solvable with ordinary numerical methods. Around the mid-1990's however, new numerical methods were developed called Markov Chain Monte Carlo methods that enabled quantitative solutions developed using Bayesian statistics for real world problems. Attwater consultants are recognized internationally as experts in solving problems and decision making using Bayesian statistics via Markov Chain Monte Carlo methods. Numerous of our published papers used Bayesian statistics and Markov Chain Monte Carlo to solve problems that were heretofore not satisfactorily solved. We have developed methods for using Bayesian statistics to avoid any questionable assumptions, which leads to the ultimate in unbiased results. We use Bayesian statistics exclusively, and have achieved quick, easy, and comfortable decision making for our clients 100% of the time. We teach Bayesian statistics in our education and training offerings as well, and can teach your staff how to make much better engineering, systems engineering, and project management decisions. Attwater Consultants provide unique and powerful skills for good decision making in engineering, systems engineering, and project management through use of Bayesian statistics and Markov Chain Monte Carlo methods. Contact
Us for more information on how an Attwater Consultant can help
fine tune your Projects into assured successes. We selectively
accept short term and long term assignments all over the world; language
used is English. |