Hierarchical Bayesian Modeling of Survey Data with Post-stratification (Tarmo Jüristo) скачать в хорошем качестве

Hierarchical Bayesian Modeling of Survey Data with Post-stratification (Tarmo Jüristo)

Speakers: Tarmo Jüristo, Alex Andorra and Thomas Wiecki Event Description In this panel discussion, Tarmo Jüristo will tell us how Bayesian modeling can help in environments where data are noisy and uncertainty is high – like public opinion polls. In particular, data can be sparse in some strata of the population, making the model’s job harder, precisely for the demographics you’re the most interested in. A special focus will be placed on the work PyMC Labs has done with Tarmo, implementing a state-of-the-art hierarchical Bayesian model. Coupled with post-stratification, this method not only makes inference possible – it makes it actionable, even you have only a few data points for some demographics. The panel discussion will be followed by Q&A. Timestamps 00:00 Thomas Wiecki does introduction and background 03:45 Tarmo introduces himself 05:20 Panel discussion starts 06:11 Description of Salk 08:13 Zooming into the data Salk uses 10:04 A look into what Salk does 13:58 Multilevel regression with post-stratification 16:27 Further refinements of the Multilevel regression with post-stratification 19:57 Model output 25:50 Question: On a multilevel aspect, does this mean you model other clusters/groups within other clusters/groups? 28:43 Input to simulation 32:20 Final simulation 34:46 Alex Andorra introduces himself 36:40 Question: How do you choose whether it makes sense to add interactions to a model and do you start with all possible interactions? 38:56 Technical difficulties during the project 46:59 Demonstration of the dashboard 51:52 You can use geospatial covariation to extend the model 53:27 Does the forecasting take the difference in policies between parties 54:19 Using Gaussian Processes in the model(Advantages and disadvantages) 59:55 Question: If you have more time, what would you add to the model 1:02:56 Question: How well do you think the model is taking without rare events? 1:06:57 Thank you! #BayesianModeling #SurveyDataAnalysis #PostStratification #HierarchicalModels #StatisticalInference #DataScience #SamplingMethods #mcmc #MultilevelModeling #BayesianStatistics #StratifiedSampling #DataAnalysis #PopulationInference #SurveySampling #PriorDistribution #StatisticalModels #BayesianInference #SamplingBias #DataStratification #BayesianEstimation About the speaker Thomas Wiecki Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world class team of Bayesian modelers founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience. Website: https://twiecki.io/ Tarmo Jüristo Tarmo is the founder of SALK, a foundation established to support progressive political forces in Estonia by providing sophisticated yet reliable data-based insights and analysis. LinkedIn:   / tarmo-j%c3%bcristo-7018bb7   Alexandre Andorra By day, Alex is a principal data scientist and co-founder at the PyMC Labs consultancy. By night, he doesn’t (yet) fight crime, but he’s an open-source enthusiast and core contributor to the awesome Python packages PyMC and ArviZ. An always-learning statistician, he loves building models and studying elections and human behavior. He also loves Nutella a bit too much, but he doesn't like talking about it – he prefers eating it. Website: https://learnbayesstats.com/ Connecting with PyMC Labs LinkedIn:   / pymc-labs   Twitter:   / pymc_labs   YouTube:    / pymclabs   Meetup: https://www.meetup.com/pymc-labs-onli... Eventbrite: https://www.eventbrite.com/o/pymc-lab... #bayesian #surveydata #hierarchical