{
    "componentChunkName": "component---src-templates-note-note-js",
    "path": "/notes/Bayes-Rule",
    "result": {"data":{"mdx":{"body":"var _excluded = [\"components\"];\nfunction _extends() { _extends = Object.assign ? Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n/* @jsxRuntime classic */\n/* @jsx mdx */\n\nvar _frontmatter = {\n  \"title\": \"Bayes Rule\",\n  \"tldr\": \"A meta mental model\",\n  \"index\": true\n};\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n    props = _objectWithoutProperties(_ref, _excluded);\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"h1\", null, \"Bayes Rule\"), mdx(\"blockquote\", null, mdx(\"p\", {\n    parentName: \"blockquote\"\n  }, \"It is a powerful law of probability that brings in the concept of \\u2018subjectivity\\u2019 or \\u2018the degree of belief\\u2019 into the cold, hard statistical modeling.\")), mdx(\"p\", null, \"This is most practical model for decision making. It makes use of priors(knowledge or assumptions or beliefs), confidence levels and when new data is available it helps us update our initial beliefs(priors). As and when we gather more data the confidence levels keeps going up. The probability goes up or down based on whether the newly available data support our priors or counters them.\"), mdx(\"p\", null, \"Ability to recursively apply Bayes rule makes it more interesting. That is a like a meta model that we can use for increasing our confidence levels in other models.\\n\", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/notes/meta-rules\",\n    \"title\": \"meta rules\"\n  }, \"meta rules\"), \"\"), mdx(\"h2\", null, \"Further reading\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"a\", {\n    parentName: \"li\",\n    \"href\": \"https://towardsdatascience.com/bayes-rule-with-a-simple-and-practical-example-2bce3d0f4ad0\"\n  }, \"https://towardsdatascience.com/bayes-rule-with-a-simple-and-practical-example-2bce3d0f4ad0\")), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"a\", {\n    parentName: \"li\",\n    \"href\": \"https://www.mathsisfun.com/data/bayes-theorem.html\"\n  }, \"https://www.mathsisfun.com/data/bayes-theorem.html\")), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"a\", {\n    parentName: \"li\",\n    \"href\": \"https://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/\"\n  }, \"https://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/\"))));\n}\n;\nMDXContent.isMDXComponent = true;","inboundReferences":[]}},"pageContext":{"slug":"Bayes-Rule","node":{"childMdx":{"slug":"Bayes-Rule"}}}},
    "staticQueryHashes": []}