Bayes' Theorem

Explore Bayes Theorem and understand how it computes posterior probabilities from prior evidence. Learn to apply this foundation in building probabilistic binary classifiers and see practical examples including conditional probability calculations for classification tasks.

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Calculating the posterior probability

Bayes’ Theorem describes a way of finding a conditional probability when we know certain other probabilities. The following equation mathematically denotes Bayes’ Theorem:

$P(Hypothesis|Evidence)=P(Hypothesis)\cdot\frac{P(Evidence|Hypothesis)}{P(Evidence)}$

Bayes’ Theorem says we can calculate the posterior probability from a prior probability and some evidence-related modifier.

The posterior denotes what we believe about the $Hypothesis$ ...

1.Getting Started

2.Binary Classification

3.Qubit and Quantum States

4.Probabilistic Binary Classifier

5.Working with Qubits

6.Working with Multiple Qubits

Project

7.Quantum Naïve Bayes

8.Quantum Computing Is Different

9.Quantum Bayesian Networks

10.Bayesian Inference

11.The World Is Not a Disk

12.Working with the Qubit Phase

13.Search for Relatives

14.Sampling

15.Conclusion

16.APPENDIX

Assessment

Bayes' Theorem