Suppose that we would like to find out again if our friend would like to play chess in the park 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, we wonder how the answer will change with this different data.
Analysis:
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 events are independent...