Bayesian Hierarichal Model

Introduction

A Bayesian hierarchical model, is a statistical model that incorporates multiple levels of variability or hierarchy in its structure. It is particularly useful when dealing with complex data that exhibits nested structures or when there is a need to model variability at different levels of aggregation. For instance, in forecasting demand for products in a retail chain, a hierarchical Bayesian model may include hierarchical components at multiple levels, such as product-level trends, store-level seasonality, and regional-level effects. By accounting for the hierarchical nature of the data, the model can improve the accuracy of demand forecasts and capture variability across different levels of aggregation.

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Transformers

Introduction

In recent years, transformers have emerged as a powerful architecture in the field of machine learning, revolutionizing natural language processing (NLP), computer vision, and other domains. With their ability to capture long-range dependencies and context, transformers have become the backbone of many state-of-the-art models. In this blog, we’ll explore what transformers are, how they work, and their applications in machine learning.

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