Data analysis Piecewise-constant linear regression Hidden Markov - PowerPoint PPT Presentation

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Data analysis Piecewise-constant linear regression Hidden Markov Models Let x t denote the log-transformed data and u t the underlying signal. A transition kernel accounting for shift and drift Hidden state space: grid with Algorithmic

Data analysis
Piecewise-constant linear regression
Hidden Markov Models Let x t denote the log-transformed data and u t the underlying signal.
A transition kernel accounting for shift and drift Hidden state space: grid with
Algorithmic complexity O ( nK 2 ) Take the example of Forward-Backward.
Impact of the grid step h = UG� BT� /1]TJ111110Tf 125.754 -90996d [( UGmaxBT� /1]53.9701 Tf 12.1.79 90996d [( UG� TJ/F48 7.9701 Tf 1252287 Td [( =) � BT� /1]TJ111110Tf 125.754 -1.514d [( UGminBT� /� ET� 1 0 0 1 0 141.5156.275 cm� 0 []0 d 1 J 90436 w 0 1 m 40.013 1 [ 0 362 Td [( =) � g7Tf 12.711 4.8955 T88m� 05 T000TJ/FK.9701 Tf 12.1.79 906 1 9� BT� /1]T48 7.9701 Tf 1252287 Td [( =) � BT� /1]T11 0 0g 0 G� 0 g 0 G� 0 g 0 G� 0 g 0 G� 0 g 0 G� 0 g 0 G� BT� /F24 10 1 141.515628.346 75UG� 4d 1 835.3535241.515.3535241.51 1 83249.449 cm51 1 /Im10 Do [( 8 G� =) ]T.) ]T.25 rg 0 =) ]T.) ]T.25 RG3249.449 -28.346 -75UG� 4d 1 .9091 Tf8.96647 Td28.346 575 000TJ/FLL
� � Model comparison Cross-Validated Log-Likelihood for robust assessment of model fitness. Model d � s
Correlation between breakpoints and biological features

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