How deeply does Sutskever's List cover bayesian statistics?

Defines priors, likelihoods, and posteriors, notes prior controversy, and stops short of a formal derivation.

Bayesian statistics

Bayes' rule updates the probability of a hypothesis from its prior probability and the likelihood of observed data under that hypothesis.

Bayesian inference updates a hypothesis’s probability by combining its prior plausibility with how well it explains observed data. In inductive inference, this turns candidate explanations into probabilities, while making the choice of prior an important and contested question. section 9.1.2

Predicting coin flips

For an unknown coin-flip process, consider hypotheses such as a fair coin, a biased coin, or a fixed repeating pattern. As flips arrive, update each hypothesis according to how well it fits the observations. section 9.1.2

How Sutskever's List covers it

liveBook manning.com

Read Sutskever's List on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Grokking Machine LearningA probability identity that expresses a conditional probability in terms of a reversed conditional probability and the relevant prior probabilities.
liveBook manning.com

Math and Architectures of Deep LearningA parameter-estimation approach that combines observed data with a prior distribution to produce a posterior distribution over unknown quantities.
liveBook manning.com

Fabulous Adventures in Data Structures and AlgorithmsBayesian inference updates probability distributions over hypotheses or parameters using observed evidence and Bayes' rule.
liveBook manning.com

Bayesian Optimization in ActionA Bayesian update is the revision of a prior belief into a posterior belief using observed data and a likelihood.
liveBook manning.com

Causal AIBayesian inference is statistical inference that updates prior beliefs about unknown quantities using observed data and a likelihood model.
liveBook manning.com

Effective Data AnalysisBayesian statistics is an approach to statistical inference that updates probability distributions for hypotheses or parameters using observed evidence.
liveBook manning.com

Inside AIBayes' theorem is a probability formula for updating the probability of a hypothesis using new evidence and prior probabilities.

Compare how 8 books explain bayesian statistics →

Related concepts

posterior probabilityproduces · A posterior probability or distribution expresses the probability assigned to an event, hypothesis, or unknown quantity conditional on observed evidence and prior information.
conditional probabilityuses · Conditional probability is the probability of an event or variable value given that specified information, an event, or another variable’s value is known.
causationcontrasts with · Causation is a relationship in which one event, condition, or factor produces or influences a change in another.
prior probabilityuses · A prior probability distribution represents beliefs or assumptions about an unknown quantity before relevant observations or evidence are incorporated.
naive bayes classifierused by · A Naive Bayes classifier is a probabilistic classification model that applies Bayes’ theorem while assuming features are conditionally independent given the class.
probability theoryuses · Probability theory is the branch of mathematics that formalizes uncertainty and analyzes random events and outcomes using probability measures.

This explainer was generated by Manning's knowledge graph from the text of Sutskever's List. The section links point to the parts of the book it draws on.