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10.1080/01621459.2014.998762 http://scihub22266oqcxt.onion/10.1080/01621459.2014.998762 C4867884!4867884!27226674     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Stat+Assoc 2016 ; 111 (513): 200-15 Nephropedia Template TP {{tp|p=27226674|t=2016. Statistical Inference in Hidden Markov Models Using k-Segment Constraints |pdf=|usr=}}{{27226674}} gab.com Text Statistical Inference in Hidden Markov Models Using k-Segment Constraints JO: J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=27226674 #FOAvip Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward?backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths. We collectively call these recursions k-segment algorithms and illustrate their utility using simulated and real examples. We also highlight the prospective and retrospective use of k-segment constraints for fitting HMMs or exploring existing model fits. Supplementary materials for this article are available online. Twit Text FOAVip Statistical Inference in Hidden Markov Models Using k-Segment Constraints ????J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=27226674 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27226674 #FOAvip English Wikipedia <ref>{{Cite pmid|27226674}}</ref> Statistical Inference in Hidden Markov Models Using k-Segment Constraints #MMPMID27226674 Titsias MK; Holmes CC; Yau C J Am Stat Assoc 2016[Jan]; 111 (513): 200-15 PMID27226674show ga Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward?backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths. We collectively call these recursions k-segment algorithms and illustrate their utility using simulated and real examples. We also highlight the prospective and retrospective use of k-segment constraints for fitting HMMs or exploring existing model fits. Supplementary materials for this article are available online. äStatistical Inference in Hidden Markov Models Using k-Segment Constrai(epmc).pdf DeepDyve Pubget Overpricing