Probability distribution functions (PDFs) are at the heart of machine learning, offering a mathematical foundation for understanding and predicting outcomes. They help you grasp the likelihood of ...

Slide 12: Cumulative Distribution Function (CDF) The Cumulative Distribution Function (CDF) gives the probability that a random variable is less than or equal to a certain value. It's useful for ...

It is really getting imperative to understand whether Machine Learning (ML) algorithms improve the probability of an event or predictability of an outcome. While the former is just a chance that an ...

For further exploration of the binomial distribution and its applications in machine learning, consider these peer-reviewed articles from arXiv.org: "On the Convergence of the Mean-Field and Binomial ...

Abstract: We present a theoretical framework of probabilistic learning derived from the maximum probability (MP) theorem shown in this article. In this probabilistic framework, a model is defined as ...

Abstract: We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even ...

The Poisson distribution is widely used in artificial intelligence (AI) and machine learning. In Bayesian inference, probability distributions often help solve problems that would otherwise be ...

In machine learning, probability distributions are used to model data. This involves fitting a distribution to the data points and using it to make predictions.