Representative vs. Random Samples: Key Differences Explained

Learn how representative samples and random samples differ and why combining them minimizes sampling bias for accurate data.
JSTOR Daily

Estimation and Inference of Quantite Regression for Survival Data Under Biased Sampling

Biased sampling occurs frequently in economics, epidemiology, and medical studies either by design or due to data collecting mechanism. Failing to take into account the sampling bias usually leads to ...
JSTOR Daily

Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments

This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Adopting a Neymanian repeated sampling approach ...
EDN

Purpose-built AI inference architecture: Reengineering compute design

Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed ...
Hosted on MSN

What Is Inference? The Bridge Between Clues and Conclusions

We all have the habit of trying to guess the killer in a movie before the big reveal. That’s us making inferences. It’s what happens when your brain connects the dots without being told everything ...
VentureBeat

How Snowflake's open-source text-to-SQL and Arctic inference models solve enterprise AI's two biggest deployment headaches

Snowflake has thousands of enterprise customers who use the company's data and AI technologies. Though many issues with generative AI are solved, there is still lots of room for improvement. Two such ...