<p> </p><p>최근에 베이지안 통계기법으로 회귀분석을 하는 자료를 공부해는데 여러분들과도 좋은 자르를 공유하고자 합니다.</p><p> </p><p><a href="https://www.pymc.io/projects/docs/en/latest/learn/core_notebooks/GLM_linear.html" target="_blank" class="ke-link">https://www.pymc.io/projects/docs/en/latest/learn/core_notebooks/GLM_linear.html</a>  </p><p> </p><p><a href="https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-robust.html" target="_blank" class="ke-link">https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-robust.html</a></p><p> </p><p><a href="https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-binomial-regression.html" target="_blank" class="ke-link">https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-binomial-regression.html</a></p><p> </p><p><a href="https://towardsdatascience.com/bayesian-regression-using-pymc3-e279ccc81d20/" target="_blank" class="ke-link">https://towardsdatascience.com/bayesian-regression-using-pymc3-e279ccc81d20/</a></p><div class="figure-open" contenteditable="false" data-ke-type="opengraph" data-ke-align="alignCenter" data-og-type="website" data-og-title="Bayesian Regression Using PyMC3 | Towards Data Science" data-og-description="How to implement Bayesian Regression in Python using the PyMC3 package" data-og-host="towardsdatascience.com" data-og-source-url="https://towardsdatascience.com/bayesian-regression-using-pymc3-e279ccc81d20/" data-og-url="https://towardsdatascience.com/bayesian-regression-using-pymc3-e279ccc81d20/" data-og-image="https://scrap.kakaocdn.net/dn/boObwk/dJMb9frFVZw/TyQ881bru4J5TT3EeZnpm0/img.jpg?width=2560&height=1707&face=0_0_2560_1707,https://scrap.kakaocdn.net/dn/vPgwc/dJMb9jOnpwv/5HeZpyLUNLn3wcUfUKMVSK/img.jpg?width=2560&height=1707&face=0_0_2560_1707,https://scrap.kakaocdn.net/dn/yIcRX/dJMb9jOnpwu/919Tvlxu6qm7hdhKvC6khk/img.png?width=1169&height=310&face=0_0_1169_310"><a href="https://towardsdatascience.com/bayesian-regression-using-pymc3-e279ccc81d20/" target="_blank" data-source-url="https://towardsdatascience.com/bayesian-regression-using-pymc3-e279ccc81d20/"><div class="og-image"><img class="thumb_img" src="https://scrap.kakaocdn.net/dn/boObwk/dJMb9frFVZw/TyQ881bru4J5TT3EeZnpm0/img.jpg?width=2560&height=1707&face=0_0_2560_1707,https://scrap.kakaocdn.net/dn/vPgwc/dJMb9jOnpwv/5HeZpyLUNLn3wcUfUKMVSK/img.jpg?width=2560&height=1707&face=0_0_2560_1707,https://scrap.kakaocdn.net/dn/yIcRX/dJMb9jOnpwu/919Tvlxu6qm7hdhKvC6khk/img.png?width=1169&height=310&face=0_0_1169_310" alt="" xxonerror="this.src="//img1.kakaocdn.net/thumb/C200x200/?fname=https%3A%2F%2Ft1.daumcdn.net%2Fcafe_image%2Fcafe_meta_image_190529.png""></div><div class="og-text"><p class="og-title">Bayesian Regression Using PyMC3 | Towards Data Science</p><p class="og-desc">How to implement Bayesian Regression in Python using the PyMC3 package</p><p class="og-host">towardsdatascience.com</p></div></a></div><p> </p><p><a href="https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-rolling-regression.html" target="_blank" class="ke-link">https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-rolling-regression.html</a></p><p> </p><p><a href="https://ddangchani.github.io/pymc/" target="_blank" class="ke-link">https://ddangchani.github.io/pymc/</a></p><div class="figure-open" contenteditable="false" data-ke-type="opengraph" data-ke-align="alignCenter" data-og-type="website" data-og-title="DDangchani's DataLog" data-og-description="통계학과 데이터 사이언스를 학습하며 얻은 내용들을 공유합니다." data-og-host="ddangchani.github.io" data-og-source-url="https://ddangchani.github.io/pymc/" data-og-url="https://ddangchani.github.io/" data-og-image="https://scrap.kakaocdn.net/dn/mqB6c/dJMb9g5bCTa/9zPPZOTu7dohQacCLvFRsK/img.png?width=402&height=398&face=0_0_402_398"><a href="https://ddangchani.github.io/pymc/" target="_blank" data-source-url="https://ddangchani.github.io/pymc/"><div class="og-image"><img class="thumb_img" src="https://scrap.kakaocdn.net/dn/mqB6c/dJMb9g5bCTa/9zPPZOTu7dohQacCLvFRsK/img.png?width=402&height=398&face=0_0_402_398" alt="" xxonerror="this.src="//img1.kakaocdn.net/thumb/C200x200/?fname=https%3A%2F%2Ft1.daumcdn.net%2Fcafe_image%2Fcafe_meta_image_190529.png""></div><div class="og-text"><p class="og-title">DDangchani's DataLog</p><p class="og-desc">통계학과 데이터 사이언스를 학습하며 얻은 내용들을 공유합니다.</p><p class="og-host">ddangchani.github.io</p></div></a></div><p> </p><p><a href="https://buildingenergygeeks.com/linearregression.html" target="_blank" class="ke-link">https://buildingenergygeeks.com/linearregression.html</a></p><div class="figure-open" contenteditable="false" data-ke-type="opengraph" data-ke-align="alignCenter" data-og-type="website" data-og-title="Linear regression (PyMC) — Building energy probabilistic modelling" data-og-description="This tutorial will serve as an introduction to PyMC. It is not a complete course on how this library works, but links to the relevant parts of the PyMC documentation will be provided. We also cover some basics of the ArviZ library. Posterior predictive dis" data-og-host="buildingenergygeeks.com" data-og-source-url="https://buildingenergygeeks.com/linearregression.html" data-og-url="https://buildingenergygeeks.com/linearregression.html" data-og-image="https://scrap.kakaocdn.net/dn/lc615/dJMb8SpIq07/fXk6HLw0A3AY886VyCbIQk/img.png?width=950&height=526&face=0_0_950_526,https://scrap.kakaocdn.net/dn/oJEO9/dJMb8QemFzv/rK1UVkVa82C4gVV2MGnr61/img.png?width=868&height=371&face=0_0_868_371,https://scrap.kakaocdn.net/dn/ckObdf/dJMb8SpIq08/qsoTxfvY8bTGKn5l2fnAkK/img.png?width=855&height=371&face=0_0_855_371"><a href="https://buildingenergygeeks.com/linearregression.html" target="_blank" data-source-url="https://buildingenergygeeks.com/linearregression.html"><div class="og-image"><img class="thumb_img" src="https://scrap.kakaocdn.net/dn/lc615/dJMb8SpIq07/fXk6HLw0A3AY886VyCbIQk/img.png?width=950&height=526&face=0_0_950_526,https://scrap.kakaocdn.net/dn/oJEO9/dJMb8QemFzv/rK1UVkVa82C4gVV2MGnr61/img.png?width=868&height=371&face=0_0_868_371,https://scrap.kakaocdn.net/dn/ckObdf/dJMb8SpIq08/qsoTxfvY8bTGKn5l2fnAkK/img.png?width=855&height=371&face=0_0_855_371" alt="" xxonerror="this.src="//img1.kakaocdn.net/thumb/C200x200/?fname=https%3A%2F%2Ft1.daumcdn.net%2Fcafe_image%2Fcafe_meta_image_190529.png""></div><div class="og-text"><p class="og-title">Linear regression (PyMC) — Building energy probabilistic modelling</p><p class="og-desc">This tutorial will serve as an introduction to PyMC. It is not a complete course on how this library works, but links to the relevant parts of the PyMC documentation will be provided. We also cover some basics of the ArviZ library. Posterior predictive dis</p><p class="og-host">buildingenergygeeks.com</p></div></a></div><p> </p><p><a href="https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/multilevel_modeling.html" target="_blank" class="ke-link">https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/multilevel_modeling.html</a></p><p> </p><p> </p><p> </p><p> </p><p>베이지안 관련 참고서는</p><p> </p><p><a href="https://sites.google.com/site/doingbayesiandataanalysis" target="_blank" class="ke-link">https://sites.google.com/site/doingbayesiandataanalysis</a></p>
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