article thumbnail

What are Joint, Marginal, and Conditional Probability?

Analytics Vidhya

Probability is a cornerstone of statistics and data science, providing a framework to quantify uncertainty and make predictions. Understanding joint, marginal, and conditional probability is critical for analyzing events in both independent and dependent scenarios. What is Probability?

article thumbnail

What is Monte Carlo Simulation in Excel?

Analytics Vidhya

Inspired by the chance and excitement of the Monte Carlo Casino in Monaco, this powerful statistical method transforms the uncertainty of life into a tool for making informed decisions. Introduction Imagine being able to predict the future with a roll of the dice—sounds intriguing, right? Welcome to the world of Monte Carlo simulation!

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Handling uncertainty: panic vs. precautions…

Timo Elliott

Researchers, of course, try to use sophisticated statistical techniques to get around these problems, and have attempted to provide their best estimates for outbreaks around the world. A more flexible way of attacking uncertainty is to look beyond specific models and instead benchmark against “other people like us.”

article thumbnail

Uncertainties: Statistical, Representational, Interventional

The Unofficial Google Data Science Blog

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

article thumbnail

2021 Data/AI Salary Survey

O'Reilly on Data

There was a lot of uncertainty about stability, particularly at smaller companies: Would the company’s business model continue to be effective? Economic uncertainty caused by the pandemic may be responsible for the declines in compensation. Average salary by tools for statistics or machine learning. What about Kafka? (See

article thumbnail

The trinity of errors in applying confidence intervals: An exploration using Statsmodels

O'Reilly on Data

Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions.

article thumbnail

What you need to know about product management for AI

O'Reilly on Data

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself.