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UID:www.tcs.tifr.res.in/event/301
DTSTAMP:20230914T125918Z
SUMMARY:State-independent Importance Sampling for Regularly Varying Random 
 Walks
DESCRIPTION:Speaker: Karthyek Rajhaa A M\n\nAbstract: \nEfficient simulatio
 n of rare events involving sums of heavy-tailed random variables has been 
 an active research area in applied probability in the last fifteen years. 
 These rare events arise in many applications including telecommunications\
 , computer and communication networks\, insurance and finance. These probl
 ems are viewed as challenging\, since large deviations theory inspired and
  exponential twisting based importance sampling algorithms that work well 
 for rare events involving sums of light tailed random variables fail in th
 ese settings.\nIn this talk we shall discuss about developing some simple 
 state-independent exponential twisting based importance sampling methods t
 o efficiently estimate such rare event probabilities. Specifically\, we de
 velop strongly efficient algorithms for estimating:\n1. The classical larg
 e deviations probability that the sums of independent\, identically distri
 buted random variables with regularly varying tails exceed an increasing t
 hreshold both in the case where the number of random variables increases t
 o infinity and when it is fixed.\n2. Finite-horizon level crossing probabi
 lities for negative-mean regularly varying random walks\nAccurate computat
 ion of these level crossing probabilities has applications in estimating r
 uin probabilities in insurance settings and calculating waiting times in G
 I/G/1 queues.\n
URL:https://www.tcs.tifr.res.in/web/events/301
DTSTART;TZID=Asia/Kolkata:20120831T110000
DTEND;TZID=Asia/Kolkata:20120831T120000
LOCATION:D-405 (D-Block Seminar Room)
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