{ localUrl: '../page/bayes_waterfall_diagram.html', arbitalUrl: 'https://arbital.com/p/bayes_waterfall_diagram', rawJsonUrl: '../raw/1wy.json', likeableId: 'LisaReed', likeableType: 'page', myLikeValue: '0', likeCount: '10', dislikeCount: '0', likeScore: '10', individualLikes: [ 'MarianAndrecki', 'JaimeSevillaMolina', 'IanPitchford', 'NateSoares', 'IvoNascimento', 'AkeemBacchus', 'EszterArat', 'JeanneLai', 'AlisaKuznetsova', 'JudeRyan' ], pageId: 'bayes_waterfall_diagram', edit: '15', editSummary: '', prevEdit: '10', currentEdit: '15', wasPublished: 'true', type: 'wiki', title: 'Waterfall diagram', clickbait: 'Visualizing Bayes' rule as the mixing of probability streams.', textLength: '2459', alias: 'bayes_waterfall_diagram', externalUrl: '', sortChildrenBy: 'likes', hasVote: 'false', voteType: '', votesAnonymous: 'false', editCreatorId: 'EliezerYudkowsky', editCreatedAt: '2016-09-29 19:12:43', pageCreatorId: 'EliezerYudkowsky', pageCreatedAt: '2016-02-07 23:35:32', seeDomainId: '0', editDomainId: 'AlexeiAndreev', submitToDomainId: '0', isAutosave: 'false', isSnapshot: 'false', isLiveEdit: 'true', isMinorEdit: 'false', indirectTeacher: 'false', todoCount: '0', isEditorComment: 'false', isApprovedComment: 'true', isResolved: 'false', snapshotText: '', anchorContext: '', anchorText: '', anchorOffset: '0', mergedInto: '', isDeleted: 'false', viewCount: '39933', text: '[summary: Waterfall diagrams, like [560 frequency diagrams], provide a way of visualizing [1lz Bayes' Rule]. For example, if 20% of the patients in the screening patient are sick (red) and 80% are healthy (blue); and 90% of the sick patients get positive test results; and 30% of the healthy patients get positive test results, we could visualize the probability flows using the following diagram:\n\n![Waterfall diagram](https://i.imgur.com/CXsoZhA.png?0)]\n\nWaterfall diagrams, like [560 frequency diagrams], provide a way of visualizing [1lz Bayes' Rule]. Recall that Bayes' rule (in the [1x5 odds form]) says that the posterior odds between any two hypotheses is equal to the prior odds times the relative likelihoods.\n\nFor example, in the [22s Diseasitis] problem, a patient is 20% likely to be sick (and 80% likely to be healthy) a priori, and they take a test that is 3x more likely to come back positive if they are sick. The odds of a patient being sick _given_ a positive test are thus $(1 : 4) \\times (3 : 1) = (3 : 4).$\n\nUsing waterfall diagrams, we can visualize the prior odds as two separate streams of water at the top of a waterfall, and the relative likelihoods as the proportion of water from each stream that makes it to the shared pool at the bottom. The posterior odds can then be visualized as the proportion of water in the shared pool that came from each different prior stream.\n\n![Waterfall diagram](https://i.imgur.com/CXsoZhA.png?0)\n\nSee [1x1] for a walkthrough of the diagram.\n\nWaterfall diagrams make it clear that, when calculating the posterior odds, what matters is the _relative proportion_ between how much each gallon of red water vs each gallon of blue water makes it into the shared pool. If 45% of red water and 15% of blue water made it to the bottom, that would give the same _relative proportion_ of red and blue water in the shared pool at the bottom as 90% and 30%.\n\n![Same relative proportions](https://i.imgur.com/6FOndjc.png?0)\n\nThus, it is only the relative likelihoods (and not the absolute likelihoods) that matter when calculating posterior odds.\n\nSimilarly, changing the water flows at the top of the waterfall from (20 gallons/sec red water : 80 gallons/sec blue water) to (40 gallons/sec red water : 160 gallons/sec blue water) would double the total water at bottom, but not change the relative proportions of blue and red water. So only the *relative* prior odds matter to the *relative* posterior odds.', metaText: '', isTextLoaded: 'true', isSubscribedToDiscussion: 'false', isSubscribedToUser: 'false', isSubscribedAsMaintainer: 'false', discussionSubscriberCount: '2', maintainerCount: '2', userSubscriberCount: '0', lastVisit: '2016-02-27 17:29:41', hasDraft: 'false', votes: [], voteSummary: [ '0', '0', '0', '0', '0', '0', '0', '0', '0', '0' ], muVoteSummary: '0', voteScaling: '0', currentUserVote: '-2', voteCount: '0', lockedVoteType: '', maxEditEver: '0', redLinkCount: '0', lockedBy: '', lockedUntil: '', nextPageId: '', prevPageId: '', usedAsMastery: 'true', proposalEditNum: '0', permissions: { edit: { has: 'false', reason: 'You don't have domain permission to edit this page' }, proposeEdit: { has: 'true', reason: '' }, delete: { has: 'false', reason: 'You don't have domain permission to delete this page' }, comment: { has: 'false', reason: 'You can't comment in this domain because you are not a member' }, proposeComment: { has: 'true', reason: '' } }, summaries: { Summary: 'Waterfall diagrams, like [560 frequency diagrams], provide a way of visualizing [1lz Bayes' Rule]. For example, if 20% of the patients in the screening patient are sick (red) and 80% are healthy (blue); and 90% of the sick patients get positive test results; and 30% of the healthy patients get positive test results, we could visualize the probability flows using the following diagram:\n\n![Waterfall diagram](https://i.imgur.com/CXsoZhA.png?0)' }, creatorIds: [ 'EliezerYudkowsky', 'NateSoares', 'SalilKalghatgi', 'EricRogstad' ], childIds: [ 'bayes_waterfall_diseasitis' ], parentIds: [ 'bayes_rule' ], commentIds: [ '5xj', '5xk' ], questionIds: [], tagIds: [ 'b_class_meta_tag' ], relatedIds: [], markIds: [], explanations: [ { id: '5773', parentId: 'bayes_waterfall_diagram', childId: 'bayes_waterfall_diagram', type: 'subject', creatorId: 'AlexeiAndreev', createdAt: '2016-08-02 00:20:12', level: '2', isStrong: 'true', everPublished: 'true' }, { id: '2049', parentId: 'bayes_waterfall_diagram', childId: 'bayes_waterfall_diseasitis', type: 'subject', creatorId: 'AlexeiAndreev', createdAt: 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