Hi there, how's going everybody. In this short tuitorial i'm going to show you about Multivariate Distribution which is a solid topic from Statistical Inference. I used numpy library ( Python library for numerical and scientific computing) to show that how you/we should be implementing the Multivariate Distribution.
Python 3.x --- pip install numpy --- Any IDE or Jupyter Notebook
Lemme just give you a brief description about it's theory part so you can have a crystal clear concept what's going on here. A multivariate distribution describes the probability of outcomes in scenarios involving multiple random variables. When working with such distributions in NumPy, you can handle and analyze data with multiple dimensions, capturing the relationships between different variables. IN PRACTICAL VIEWS- In practical terms, using NumPy to work with multivariate distributions typically involves creating arrays that represent multiple dimensions of data. For example, consider a dataset with multiple features such as height, weight, and age of individuals. This dataset can be represented as a two-dimensional NumPy array, where each row corresponds to an individual and each column corresponds to a different feature. Overall, using NumPy for multivariate distributions allows you to efficiently handle and analyze complex datasets, leveraging its powerful array operations and statistical functions to gain insights into the relationships between multiple variables. Thank you❤️.