Category: bayesian-statistics
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Stop Blaming the Data: A Better Way to Handle Covariance Shift
Stop Blaming the Data: A Better Way to Handle Covariance Shift Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to estimate how your model should perform in the new environment The post Stop Blaming the Data: A Better Way to Handle Covariance Shift appeared first on Towards Data Science.…
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A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python
A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python Learn Causal Structures and make inferences with Bayesian Methods: Python Tutorial The post A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python appeared first on Towards Data Science. Erdogan Taskesen Go to original source
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Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling.
Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling. Learn how to move beyond prediction and actively make intervention through prescriptive modeling. This in-depth guide walks you through Bayesian approaches to system intervention, with practical examples in predictive maintenance. The post Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling. appeared…
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Exploring New Hyperparameter Dimensions with Laplace Approximated Bayesian Optimization
Exploring New Hyperparameter Dimensions with Laplace Approximated Bayesian Optimization Is it better than grid search? Image by author from canva When I notice my model is overfitting, I often think, “It is time to regularize”. But how do I decide which regularization method to use (L1, L2) and what parameters to choose? Typically, I perform hyperparameter optimization…
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Bayesian A/B Testing Falls Short
Bayesian A/B Testing Falls Short Why Bayesian A/B testing can lead to misunderstandings, inflated false positive rates, introduce bias and complicate results (Image generated by the author using Midjourney) Over the past decade, I’ve engaged in countless discussions about Bayesian A/B testing versus Frequentist A/B testing. In nearly every conversation, I’ve maintained the same viewpoint:…
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Introduction to the Finite Normal Mixtures in Regression with
Introduction to the Finite Normal Mixtures in Regression with Introduction to the Finite Normal Mixtures in Regression with R How to make linear regression flexible enough for non-linear data The linear regression is usually considered not flexible enough to tackle the nonlinear data. From theoretical viewpoint it is not capable to dealing with them. However, we…
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Decoding the Hack behind Accurate Weather Forecasting: Variational Data Assimilation
Decoding the Hack behind Accurate Weather Forecasting: Variational Data Assimilation Learn how to implement the variational data assimilation, with mathematical details and PyTorch for efficient implementation. Continue reading on Towards Data Science » Wencong Yang, PhD Go to original source
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Addressing the Butterfly Effect: Data Assimilation Using Ensemble Kalman Filter
Addressing the Butterfly Effect: Data Assimilation Using Ensemble Kalman Filter Learn how to implement the Ensemble Kalman Filter for data assimilation, with mathematical details step-by-step code. Continue reading on Towards Data Science » Wencong Yang, PhD Go to original source
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How to Apply the Central Limit Theorem to Constrained Data
How to Apply the Central Limit Theorem to Constrained Data What can we say about the mean of data distributed in an interval [a, b]? Continue reading on Towards Data Science » Ryan Burn Go to original source
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A Story of Long Tails: Why Uncertainty in Marketing Mix Modelling is Important
A Story of Long Tails: Why Uncertainty in Marketing Mix Modelling is Important “Details matter. It’s worth waiting to get it right.” — Steve Jobs Continue reading on Towards Data Science » Javier Marin Go to original source