Quotes About Bayesian

The essential keys to disciplined Bayesian reasoning can be simply summarized: Anchor your judgment of the probability of an outcome on a plausible base rate. Question the diagnosticity of your evidence.

Bayesian reasoning recommends against the common practice of using "textbook" as an insult and "scientific revolution" as a compliment.

In the Bayesian framework, how much you believe something after you see the evidence depends not just on what the evidence shows, but on how much you believed it to begin with.

Frederick Mosteller, who would later found Harvard's statistics department, was there. So was Leonard Jimmie Savage, the pioneer of decision theory and great advocate of the field that came to be called Bayesian statistics.* Norbert Wiener, the MIT mathematician and the creator of cybernetics, dropped by from time to time. This was a group where Milton Friedman, the future Nobelist in economics, was often the

Fighting for the acceptance of Bayesian networks in AI was a picnic compared with the fight I had to wage for causal diagrams [in the stormy waters of statistics].

With Bayesian networks, we had taught machines to think in shades of grey, and this was an important step toward humanlike thinking. But we still couldn't teach machines to understand causes and effects. We couldn't explain to a computer why turning the dial of a barometer won't cause rain.... Without the ability to envision alternate realities and contrast them with the currently existing reality, a machine...cannot answer the most basic question that makes us human: "Why?

If the first three chips they withdrew from a bag were red, for instance, they put the odds at 3:1 that the bag contained a majority of red chips. The true, Bayesian odds were 27:1. People shifted the odds in the right direction, in other words; they just didn't shift them dramatically enough. Ward Edwards had coined a phrase to describe how human beings responded to new information. They were "conservative Bayesians.

Loving was playing a Bayesian game now, one modeled on imperfect information.

In a different direction, the necessity to model the analysis of noisy incomplete sensory data not by logic but by Bayesian inference first came to the forefront in the robotics community with their use of Kalman filters.