How deeply does Statistics Slam Dunk cover conditional probability?

Defines the measure through counts, computes segment probabilities, and combines them with medians for expected value.

Conditional probability

Conditional probability is the probability of an event given that a specified condition or other event holds.

Conditional probability measures how likely an outcome is after restricting attention to a specified condition. In NBA draft analysis, it answers questions such as: how likely is a future superstar given a pick in the top five? section 3.8

The calculation follows a practical workflow:

For picks one through five, the probability of selecting a future superstar is 0.24, or 12 of 50 players. section 3.8

NBA draft value

Top-five picks have a 24% conditional probability of producing a player with more than 100 career win shares, while later first-round picks have about a 1% probability. Weighting each outcome's median win shares produces expected values of 57.53 and 21.21. section 3.8

How Statistics Slam Dunk covers it

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Learn Generative AI with PyTorchA probability of one variable given another, used here for discriminative prediction of Y from X.
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Fabulous Adventures in Data Structures and AlgorithmsConditional probability is the probability of an event given that another event or condition is known to hold.
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Build a Robo-Advisor with Python (From Scratch)Conditional probability is the probability of an event given that another event or condition is known to occur.
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Math and Architectures of Deep LearningThe probability of an event computed under the assumption that another event or condition is already known to hold.
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Natural Language Processing in Action, Second EditionConditional probability is the probability of an event given that another event or condition is known to hold.
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Inside AIConditional probability is the probability of an event given that another event or condition is known to have occurred.

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Related concepts

bayesian statisticsused by · Bayesian statistics is a framework for statistical inference that combines prior probability distributions with observed-data likelihoods to update probabilities over hypotheses or unknown quantities.
independencecontrasts with · Two events or random quantities are independent when knowing the outcome or value of one does not change the probability or distribution of the other.
Pyroimplemented by · Pyro is an open-source probabilistic programming library for building probabilistic models and performing Bayesian inference with Python and PyTorch.
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.
conditional expectationequivalent to · A conditional expectation is the expected value of a random quantity given specified information, conditions, events, or values of other quantities.

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