@4591
"Poisson distribution doesn't work here" ++ How do you know?
"because if chess happened to be a forced win for white,
likelihood for the first error would be bigger than for the second error."
++ If you refine the distribution, then the result might slightly change, but not drastically.
"You cannot predict the amount of errors because each error has a different probability."
++ If probabilities of errors slightly differ, then the result will slightly differ, but not drastically.
"to avoid an error there are x amount of available lines that maintain current evaluation and the x varies from position to position." ++ That is right, but I look at the big picture, at 136 or 210 games with each like 50 moves or 100 positions.
"We cannot know the probability for the errors and we cannot make predictions for errors."
++ Yes, we can. That is the fundament of statistics.
Making comparisons is always tricky, but I try one.
Say you want to answer the question:
'How low can I make a door so all humans can pass under it upright?'
Now you cannot measure all 8 billion people.
The usual way is to measure a sufficient number of people, calculate the average and the standard deviation, then use the Gaussian distribution to calculate how high the door must be so all 8 billion can pass under it.
Now you retort: 'Gaussian distribution does not work.'
Men are generally taller than women so it is a bimodal distribution.
Adults are taller than children, so it is a skewed distribution.
In some countries people tend to be taller than in other countries.
Basketball players tend to be taller than marathon runners.
We cannot decide how high the door must be.
The comparison highlights the problem we're having. Humans could vary from say 5ft to 8ft. We have good data on how much they vary to make predictions.
Again in chess there are always x amount of lines that maintain current evaluation and avoid an error. Say that with best play there are 2 available lines that force a win for white. Once an error happens and evaluation changes from a win to a draw, there are now 19 available lines to force a draw with best play.
We don't have accurate data on how much "x" varies to make any predictions on errors per game. We could have 136 games where the first error occured in 100% of the games and the second occured in 15% of the games. There is no way to reliably make predictions
@4591
"Poisson distribution doesn't work here" ++ How do you know?
"because if chess happened to be a forced win for white,
likelihood for the first error would be bigger than for the second error."
++ If you refine the distribution, then the result might slightly change, but not drastically.
"You cannot predict the amount of errors because each error has a different probability."
++ If probabilities of errors slightly differ, then the result will slightly differ, but not drastically.
"to avoid an error there are x amount of available lines that maintain current evaluation and the x varies from position to position." ++ That is right, but I look at the big picture, at 136 or 210 games with each like 50 moves or 100 positions.
"We cannot know the probability for the errors and we cannot make predictions for errors."
++ Yes, we can. That is the fundament of statistics.
Making comparisons is always tricky, but I try one.
Say you want to answer the question:
'How low can I make a door so all humans can pass under it upright?'
Now you cannot measure all 8 billion people.
The usual way is to measure a sufficient number of people, calculate the average and the standard deviation, then use the Gaussian distribution to calculate how high the door must be so all 8 billion can pass under it.
Now you retort: 'Gaussian distribution does not work.'
Men are generally taller than women so it is a bimodal distribution.
Adults are taller than children, so it is a skewed distribution.
In some countries people tend to be taller than in other countries.
Basketball players tend to be taller than marathon runners.
We cannot decide how high the door must be.