Member-only story

CS(17) Introduction to Statistical Inference and Simulation

Carla Martins
CodeX
Published in
4 min readDec 4, 2024

Statistical inference is a critical component of data analysis. It focuses on making conclusions about a population based on information obtained from a sample. The main goal is to estimate unknown parameters or test hypotheses about the population’s characteristics. This process relies on probability theory to quantify uncertainty and ensure the reliability of the results.

In many real-world situations, analytical solutions for statistical inference problems are too complex or impossible to derive. For instance, in high-dimensional data analysis or when dealing with intricate probability distributions, calculating exact probabilities or parameter estimates analytically can be overwhelming. This is where simulation techniques, especially Monte Carlo become indispensable.

Key Question in Statistical Inference

When we aim to estimate a parameter 𝜽 of a random variable X based on a sample X1, X2, … Xn two fundamental questions arise:

Which estimator should we use for 𝜽?

How can we evaluate the quality of this estimator? This involves understanding properties like bias, consistency, and efficiency.

For example, suppose we want to estimate the mean 𝞵 of a population. While the sample mean is…

--

--

CodeX
CodeX

Published in CodeX

Everything connected with Tech & Code. Follow to join our 1M+ monthly readers

Carla Martins
Carla Martins

Written by Carla Martins

Compulsive learner. Passionate about technology. Speaks C, R, Python, SQL, Haskell, Java and LaTeX. Interested in creating solutions.

No responses yet