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Everything about Algorithmic probability
Mathematical method of assigning a prior probability to a given observation
In algorithmic information theory, algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the method together with Bayes' rule to obtain probabilities of prediction for an algorithm's future outputs. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Algorithmic probabilityA 5-minute orientation
- Skim a recent paper: Predicting phenotype transition probabilities via conditional algorithmic probability approximationsInfluential work since 2022
- Pick up a classic book: The EM algorithm and related statistical modelsThe most-reprinted book on the subject
- Read the landmark paper: The Discovery of Algorithmic ProbabilityCited 60 times
- Go sideways: Solomonoff's theory of inductive inferenceA closely related topic
- Go sideways: Kolmogorov complexityA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
The Discovery of Algorithmic Probability
Multiscale probability mapping: groups, clusters and an algorithmic search for filaments in SDSS
On the relation between descriptional complexity and algorithmic probability
Coding-theorem like behaviour and emergence of the universal distribution from resource-bounded algorithmic probability
Algorithmic Approach to the Extinction Probability of Branching Processes
Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences
Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces
Mitigating Algorithmic Bias Through Probability Calibration: A Case Study on Lead Generation Data
🆕 Influential since 2022
Predicting phenotype transition probabilities via conditional algorithmic probability approximations
Simplicity bias, algorithmic probability, and the random logistic map
Multiclass classification utilising an estimated algorithmic probability prior
Low Complexity, Low Probability Patterns and Consequences for Algorithmic Probability Applications
Algorithmic randomness and the weak merging of computable probability measures
📚 Books worth reading
The EM algorithm and related statistical modelsMichiko Watanabe · 2003
Approximation, Randomization, and Combinatorial Optimization. Algorithms and TechniquesPrasad Raghavendra, Sofya Raskhodnikova · 2003
Approximate Iterative AlgorithmsAnthony Louis Almudevar · 2014- 📕Discrete Problems in Nature Inspired AlgorithmsAnupam Prof Shukla, Ritu Tiwari · 2017
Introduction to Data ScienceRafael A. Irizarry · 2019
Probability and ComputingMichael Mitzenmacher, Eli Upfal · 2005
Digital DicePaul J. Nahin · 2008
Probability and Statistics with Reliability, Queuing, and Computer Science ApplicationsKishor S. Trivedi · 2008
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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- Ray Solomonoff Ray Solomonoff (July 25, 1926 – December 7, 2009) was an American mathematician who invented algorithmic probability, his General Theory of Inductive Inference…
- Algorithmic information theory Algorithmic information theory (AIT) is a branch of theoretical computer science that concerns itself with the relationship between computation and information…
- Universality probability Universality probability is an abstruse probability measure in computational complexity theory that concerns universal Turing machines. A Turing machine…
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- Inductive probability Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematica…
- AIXI AIXI /ˈaɪksi/ is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory…