• fossphi@lemm.eeEnglish
    55·
    4 months ago

    We’ve been through some rough times [4-7]

    What the heck would those citations be‽

    • Ooops@feddit.orgEnglish
      20·
      4 months ago

      [4] Broman, Chad 2015 Why is Valentine’s Day so Important? A Time Analysis of Tiffany’s Relationship Expectations :: Journal of Psychological Machine Learning

      [5] Broman, Chad 2016 A Play by Play Analysis of Purchasing a Luxury Speedboat during an out of Wedlock Pregnancy Scare :: Journal of Psychological Machine Learning

      [6] Broman, Chad 2016 The Mood Metric Equivalent Measurement: How to Get Away with a 150$ Bar Tab :: Journal of Psychological Machine Learning

      [7] Broman, Chad 2021 A Time Series Analysis of My Girlfriend’s Mood Swings :: Journal of Astrological Big Data Ecology

      • henfredemars@infosec.pubEnglish
        12·
        4 months ago

        The Nyquist theorem, in very simple terms, describes the minimum measurements you need to take to capture all the information in a signal. It turns out, if you have special information about what signal you expect to see, you can still figure it out using fewer measurements.

        Generally speaking, it tells you how many measurements you need to take to capture the whole signal.

        • grue@lemmy.worldEnglish
          8·
          4 months ago

          Or in terms of practical applications, it explains why CDs are “good enough” and audiophile stuff is very often bullshit.

      • Artyom@lemm.eeEnglish
        4·
        4 months ago

        Instead of actually explaining it, here’s how I like to remember it: If you are measuring something that produces a perfect sine wave, and you can’t take measurements faster than 30 Hz, then you can’t definitively prove that the sine wave repeats faster than that. No matter how many data points you collect, the actual frequency could always be double what your measurements suggest.