Probabilistic reasoning in intelligent systems
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences.
Stats for nerds
7recommend
11mentions
11people
2009–2024years
64% recommend36% neutral
Mentions per year
What Hacker News says
- Great book that clearly explains the perils or inferring causation and now you can deal with them constructively.
For those who want more he has a more technical book "Causality: Models, Reasoning and Inference" which is also excellent.
Pearl is a legend in the field, who wrote the seminal "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference".
There is a school of thought that there is nothing new here. Pearl is very honest and open about who invented what and of the mistakes in his earlier work. While no one finding is entirely new here, the overall package adds up to a lot IMHO. And this after having intensively studied statistics and probability over many years. It really changed my approach to and understanding of causality. And most importantly it gave me reliable intuitions on the subject. After his books, things seem obvious to me that others struggle with.
- I think "Probabilistic Reasoning in Intelligent Systems" is a better starting point for Pearl. Not much easier but more familiar ground if you're coming from today's ML mindset.
As an aside, once while reading through Wasserman's "All of Statistics", he somehow hypnotized me into seeing the title of Ch.16 as "Casual Inference", so anyone who knows me knows that I can't help making a dad-joke about casual inference when the topic comes up.
- Pearl's "Probabilistic Reasoning in Intelligent Systems" was one of the most lauded books during my time in the university. I remember when Pearl gave a talk about his upcoming book Causality, my professor said that it might become one of the most important math and philosophy books of our life time. I never read Causality, and it is now 16 years since the publication. Can anyone who follows the field comment how it is perceived today?
- Just an interesting note, his son was Daniel Pearl, who was tragically murdered in Pakistan.
I enjoyed Probabilistic Reasoning in Intelligent systems. I was unaware of Causality.
- Judea Pearl's Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. I'm not finished with it yet, though. (It might be superceded by his Causality which I'd also like to read.) It's provided some "aha", scales falling from eyes moments. I'd guess it's the most important book - for me - I've read in years, perhaps ever. Highly recommended (with the caveat about his other book perhaps being better).
- Seconded hard.
If you have to read only one of these, read Probabilistic Reasoning in Intelligent Systems.
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