Is consecutively-observed evidence stronger in inference? I'm new to Bayesian theories, and the question is about modification on 'sunrise problem'. 
The question is that, if I observed sun rise in n consecutive days, will the probability of 'sun rises tomorrow' be higher than that derived from if I observed sun in n non-consecutive days (i.e. on some day I observe, and I do not on other days so that I don't know about if the sun did rise on those days). (Intuitively, I suppose the first is a stronger condition, however, I have no clue on that.)
Similarly, under same condition from above, will the probability of sun will rise tomorrow be different from sun will rise every day? It's confusing to me since if sun would rise tomorrow, the probability for the day after tomorrow will change, isn't it? (i.e. it somehow works like a iterative method?) 
To further expose the contradiction, if the probability of 'sun will rise' is a finite number less than 1, then, obviously, the probability of 'sun will rise every day' will be 0.

Suppose you observed the sun rise on July 1st, 5th, 7th and 20th. Four non-consecutive days. I think that for your problem you are assuming that there is no data for the other days, you don't know if the sun did or did not rise.
Alternatively, you could have observed the sun rise on July 17th, 18th, 19th and 20th. Four consecutive days.
By itself the consecutive days show no stronger evidence of the sun rising tomorrow. The updating of prior probabilities will only be affected if your probability model includes some dependence on the previous day's sun. If you assume that the probability of the sun rising on one day is independent from the sun rising on previous days then the prior probabilities are updated the same way in both cases.
