Tag Bayesian Statistics
In research, the distinction between explanatory variables and response variables plays a critical role in statistical interpretation. Understanding the impact of switching these variables can lead to confusion and significant errors in statistical conclusions. This article delves into the intricacies… Continue Reading →
Understanding the impact of non-i.i.d. data in minibatch GD is crucial for practitioners in the field of machine learning. As we delve deep into this subject, we will explore what i.i.d. means, how non-i.i.d. data affects training, and the consequences… Continue Reading →
In the ever-evolving world of statistics and machine learning, the quest for efficient and accurate methods for estimating posterior distributions is relentless. Among these methods, variational inference has gained significant traction. However, an important question arises: How can we effectively… Continue Reading →