Suppose that we would like to find out whether our friend would like to play chess with us in a park in Cambridge, UK. But, this time, we are given different input data:
Temperature | Wind | Season | Play |
Cold | Strong | Winter | No |
Warm | Strong | Autumn | No |
Warm | None | Summer | Yes |
Hot | None | Spring | No |
Hot | Breeze | Autumn | Yes |
Warm | Breeze | Spring | Yes |
Cold | Breeze | Winter | No |
Cold | None | Spring | Yes |
Hot | Strong | Summer | Yes |
Warm | None | Autumn | Yes |
Warm | Strong | Spring | ? |
So, now we are wondering how the answer to whether our friend would like to play in a park in Cambridge, UK, will change with this different data in regard to the Temperature being Warm, the Wind being Strong, and the Season being Spring.
We may be tempted to use Bayesian probability to calculate the probability of our friend playing chess with us in the park. However, we should be careful, and ask whether the probability of the events are independent of each other.
In the previous example, where we used Bayesian probability, we were given the probability variables Temperature...