Pearl won the Turing Award for his work applying Bayesian analysis to machine learning, among other accomplishments.
For HNers just out of college, it's important to note that Bayesian analysis has not always been as popular as it is now. In fact, even as recently as the 1990s, it was regarded with suspicion by many statisticians, who strongly disliked the idea that prior and posterior distributions are meant to represent subjective states of belief. I was fortunate enough to have a very progressive statistics professor in undergrad in the 1990s, who was interested in Bayesian analysis. It's my understanding that most upper-level statistics and probability coursework avoided doing much Bayesian analysis until around the turn of the millennium. (if you went to university in the 1990s or earlier, was this your experience?).
For those interested in learning more about this topic, an excellent book on the history of the Bayes theorem controversies was recently published by Yale Unviersity Press: _The Theory That Would Not Die_ by Sharon Bertsch McGrayne
To be fair to Judea Pearl, I do see that he wrote an essay entitled, "Bayesianism and causality, or, why I am only half-bayesian". Nonetheless, much of his work appears to involve what we now know as Bayesian analysis. So like many (all?) scientists who make major breakthroughs, Judea Pearl was going against the accepted understanding of probability by pushing Bayesian analysis in the 70s, 80s, and 90s. It's good to see his daring rewarded.
Part of the reason Bayesian analysis was avoided was because, prior to Pearl's work, inference was impractical in non-toy examples. A major contribution of Pearl's work was to make it feasible, by structuring the probability distributions as Bayesian networks that limited the possible dependencies, coupled with the belief-propagation algorithm to do approximate updates.
I don't see Pearl as primarily interested in the Bayesian v. frequentist debate himself, though, but rather in how to efficiently do probabilistic reasoning in non-trivial problems in general, with a heavy tilt towards questions of representing causality. Methodologically his work over the years has used all sorts of things from various camps; for example, he was also an authority in the early 1980s on heuristic search.
I'm trying to persuade people to abandon the terminology of "subjective" versus "objective" and talk instead of "individual" versus "situational" versus "transcendental". If the simple counting argument (that you cannot talk sensibly about three possibilities using only two words) does not convince, worry instead that law and science put the subjective/objective boundary in different places.
For HNers just out of college, it's important to note that Bayesian analysis has not always been as popular as it is now. In fact, even as recently as the 1990s, it was regarded with suspicion by many statisticians, who strongly disliked the idea that prior and posterior distributions are meant to represent subjective states of belief. I was fortunate enough to have a very progressive statistics professor in undergrad in the 1990s, who was interested in Bayesian analysis. It's my understanding that most upper-level statistics and probability coursework avoided doing much Bayesian analysis until around the turn of the millennium. (if you went to university in the 1990s or earlier, was this your experience?).
For those interested in learning more about this topic, an excellent book on the history of the Bayes theorem controversies was recently published by Yale Unviersity Press: _The Theory That Would Not Die_ by Sharon Bertsch McGrayne
To be fair to Judea Pearl, I do see that he wrote an essay entitled, "Bayesianism and causality, or, why I am only half-bayesian". Nonetheless, much of his work appears to involve what we now know as Bayesian analysis. So like many (all?) scientists who make major breakthroughs, Judea Pearl was going against the accepted understanding of probability by pushing Bayesian analysis in the 70s, 80s, and 90s. It's good to see his daring rewarded.