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Compute Gaussian Probability and Regression Coefficients

Last updated: May 2, 2026

Quick Overview

This question evaluates probabilistic reasoning with Gaussian variables and parameter estimation in ordinary least squares regression, covering deriving probabilities for linear combinations of standard normals and computing regression coefficients via linear algebra.

  • medium
  • Point72
  • Machine Learning
  • Machine Learning Engineer

Compute Gaussian Probability and Regression Coefficients

Company: Point72

Role: Machine Learning Engineer

Category: Machine Learning

Difficulty: medium

Interview Round: Technical Screen

You are given two independent standard normal random variables, X and Y. 1. Compute P[X > 3Y]. 2. In ordinary linear regression with design matrix X in R^{n x p}, response vector y in R^n, and coefficient vector beta in R^p, how do we estimate beta? 3. If n is much larger than p, how can beta be computed efficiently and exactly?

Quick Answer: This question evaluates probabilistic reasoning with Gaussian variables and parameter estimation in ordinary least squares regression, covering deriving probabilities for linear combinations of standard normals and computing regression coefficients via linear algebra.

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|Home/Machine Learning/Point72

Compute Gaussian Probability and Regression Coefficients

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Point72
Feb 25, 2026, 12:00 AM
mediumMachine Learning EngineerTechnical ScreenMachine Learning
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0

You are given two independent standard normal random variables, X and Y.

  1. Compute P[X > 3Y].
  2. In ordinary linear regression with design matrix X in R^{n x p}, response vector y in R^n, and coefficient vector beta in R^p, how do we estimate beta?
  3. If n is much larger than p, how can beta be computed efficiently and exactly?
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