philippe weber tfzplvcrnawmoesstinuhpnancyfr dear wei li work subject dynamic bayesian networks applied reliability manufacturing systems dbn based iterative inferences keep compact network form solution used following paper liste possible compute probability distribution variable xi time step k1 based probabilities corresponding time step k probability distributions time step k2 computed using successive inferences welch thelen 2000 network called 2tbn boyen koller 1998 two time slices defined first slice contains nodes corresponding current time step k second one following time step k1 node called temporal node direct link temporal clone time step k1 observations introduced hard evidence probability distributions realised current time slice k bayesialab software use simulate models propose extention n order call ntbn paper ben salem et al 2004 introduction context exogenous random variable made means node governs behaviour xi thank cpt weber et al 2004 temporal nodes independent still use exact inference algorithm otherwise use approximate inference methods example particle filtering doucet et al 2000 koller lerner 2001 exact inference used time increment carried setting computed marginal probabilities node time step k1 observations corresponding node previous time slice bayesialab software see wwwbayesiacom allows simulate model presented paper think publications relative dbn useful work exhaustive list ben salem bouillaut l aknin p weber p dynamic bayesian networks classification rail defects ieee 4th international conference intelligent systems design applications isda 2004 budapest hungary august 2628 2004 bouillaut l weber p ben salem aknin p use causal probabilistic networks improvement maintenance railway infrastructure ieee international conference systems man cybernetics hague netherlands october 1013 2004 boyen x koller 1998 tractable complex stochastic processes proceedings 14th annual conference uncertainty ai uai madison wisconsin july 1998 pp 3342 doucet n de freitas k murphy russel 2000 raoblackwellised particle filtering dynamic bayesian networks proceedings 16th annual conference uncertainty ai uai pp 176183 koller u lerner 2001 sampling factored dynamic systems sequential monte carlo methods practice doucet jfg de freitas n gordon eds springerverlag pp 445464 muller weber p ben salem process modelbased dynamic bayesian networks prognostic ieee 4th international conference intelligent systems design applications isda 2004 budapest hungary august 2628 2004 weber p jouffe l complex system reliability modelling dynamic object oriented bayesian networks doobn reliability engineering system safety volume 91 issue 2 february 2006 pages 149162 selected papers presented qualita 2003 weber p p munteanu l jouffe 2004 dynamic bayesian networks modelling dependability systems degradations exogenous constraints 11th ifac symposium information control problems manufacturing incom04 salvadorbahia brazil april 57 weber p l jouffe 2003 reliability modelling dynamic bayesian networks 5th ifac symposium fault detection supervision safety technical processes safeprocess03 washington dc usa june 911 welch r thelen 2000 dynamic reliability analysis operational context bayesian network perspective dynamic reliability future directions edited c smidts j devooght pe labeau isbn 0 9652669 3 1 maryland usa papers available sending email sincerely philippe weber wei li écrit dear im seeking feedbacksuggestions bayesian inference incrementally goal answer queries bayesian network based previous inference results structurally similar bayesian networks instead performing inference scratch structurally similar bns may obtained removing adding arc references existing work highly appreciated thanks wei _______________________________________________ uai mailing list faqengrorstedu httpssecureengroregonstateedumailmanlistinfouai wed 06 aug 2008 215640 0100 uai bayesian inference incrementally