Sampling

1. Uniform Sampling: Imagine you have a bunch of data points, like measuring the temperature every day. If you take a reading every day at the same time, that's uniform sampling. It's like clockwork, happening at regular intervals.
2. Non-Uniform Sampling: Now, imagine you're measuring the temperature, but sometimes you check it in the morning, sometimes in the afternoon, and sometimes at night. There's no fixed pattern or regularity in when you take your measurements. That's non-uniform sampling.

In non-uniform sampling:

• The time intervals between measurements are not constant.
• You might have more measurements during certain periods and fewer during others.
• It's a bit irregular, not like the steady tick-tock of a clock.

Non-uniform sampling is common in many real-world scenarios, especially when dealing with time series data collected in a less structured or systematic way. It introduces challenges in data analysis and modeling because you don't have measurements at consistent intervals.

In summary, non-uniform sampling means the timing or intervals between data points is not constant or regular. It can make analyzing and modeling the data a bit trickier compared to situations where measurements are taken at fixed, uniform intervals.