Generalised Linear Models — Basics and Implementation
Probability Distributions:
Probability distributions are fundamental to statistics, just like data structures are to computer science.
Things happen all the time:
1) dice are rolled,
2) it rains,
3) buses arrive.
After the fact, the specific outcomes are certain:
- the dice came up 3 and 4,
- there was half an inch of rain today,
- the bus took 3 minutes to arrive.
Probability distributions describe what we think the probability of each outcome is, which is sometimes more interesting to know than simply which single outcome is most likely.
They come in many shapes, but in only one size: probabilities in a distribution always add up to 1.
For example, flipping a fair coin has two outcomes: it lands heads or tails.
Before the flip, we believe there’s a 1 in 2 chance, or 0.5 probability, of heads. The same is true for tails.
That’s a probability distribution over the two outcomes of the flip, and this is an example of Bernoulli distribution.
