Efficient inference for start-up demonstration tests

Oregon Convention Center 

Auxiliary Markov chains have been used as a mechanism to efficiently compute the distribution of a pattern statistic in a Markovian sequence. However, if distributions are needed for many values of input probabilities and not just one set of values, the entire computation needs to be repeated. In this work, a method is forwarded that reduces computational burden for this scenario. Counts of data strings with various values of sufficient statistics are updated instead of probabilities. The final counts are then used to reconstruct probabilities for the many input probabilities, improving efficiency. In this talk, the methodology is illustrated on computing the probability of accepting a unit in start-up demonstration tests for many different start-up probabilities.

minimal deterministic finite automaton

sequence alignment

sequential computation

spaced seeds

sparse Markov models

start-up demonstration tests 

Section on Statistical Computing