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quant 4N


1eyedjack

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[hv=sn=1eyedjack&s=SAJ9HKT9DQ43CAK74&wn=Robot&w=SQ732HJ752D62CJ95&nn=Robot&n=SKT85HAQ43DAT5CQ8&en=Robot&e=S64H86DKJ987CT632&d=w&v=b&b=4&a=P1N(notrump%20opener.%20Could%20have%205M.%20--%202-5%20%21C%3B%202-5%20%21D%3B%202-5%20%21H%3B%202-5%20%21S%3B%2015-17%20HCP%3B%2018-%20total%20points)P4N(Quantitative%20invite%20to%206NT%20--%202-5%20%21C%3B%202-)P6N(2-5%20%21C%3B%202-5%20%21D%3B%202-5%20%21H%3B%202-5%20%21S%3B%2017%20HCP%3B%2018-%20total%20points)PPP&p=S6S9SQSKS5S4SAS7SJS2S8D7HTH7HAH8STC6D3S3D5DKD4D6D9DQD2DT&c=12]400|300[/hv]

 

Given that 6N is quite good despite that North is min, I probably should have bid 6N rather than 4N.

 

While I am grateful to North for rescuing me, should it have done so?

 

 

 

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In principle it's perfectly fine that GIB relies on simulations instead of walrus rules when it has captainship. But sometimes the sample size is too small. A Bayesian approach would be great - formulate a prior distribution for the the number of tricks that can be taken and then update that prior using the sims. I suppose this is sci-fi for the time being.
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