{"type":"conference_poster","title":"Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating","citation":{"ama":"Hernández Rodriguez T, Posch C, Schmutzhard J, et al. <i>Predicting Industrial Scale Cell Culture Seed Trains – Considerations of Input Uncertainty, New Process Data and Prognostic Intervals Using a Bayesian Approach and Sequential Bayesian Updating</i>.; 2019.","ieee":"T. Hernández Rodriguez <i>et al.</i>, <i>Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating</i>. 2019.","apa":"Hernández Rodriguez, T., Posch, C., Schmutzhard, J., Stettner, J., Weihs, J., Pörtner, R., &#38; Frahm, B. (2019). <i>Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating</i>. Presented at the DECHEMA Himmelfahrtstagung “Intensification and digitalisation for integral bioprocessing,” Hamburg, Germany.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Posch, C.</span> ; <span style=\"font-variant:small-caps;\">Schmutzhard, J.</span> ; <span style=\"font-variant:small-caps;\">Stettner, J.</span> ; <span style=\"font-variant:small-caps;\">Weihs, J.</span> ; <span style=\"font-variant:small-caps;\">Pörtner, R.</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: <i>Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating</i>, 2019","bjps":"<b>Hernández Rodriguez T <i>et al.</i></b> (2019) <i>Predicting Industrial Scale Cell Culture Seed Trains – Considerations of Input Uncertainty, New Process Data and Prognostic Intervals Using a Bayesian Approach and Sequential Bayesian Updating</i>. .","van":"Hernández Rodriguez T, Posch C, Schmutzhard J, Stettner J, Weihs J, Pörtner R, et al. Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating. 2019.","havard":"T. Hernández Rodriguez, C. Posch, J. Schmutzhard, J. Stettner, J. Weihs, R. Pörtner, B. Frahm, Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating, 2019.","mla":"Hernández Rodriguez, Tanja, et al. <i>Predicting Industrial Scale Cell Culture Seed Trains – Considerations of Input Uncertainty, New Process Data and Prognostic Intervals Using a Bayesian Approach and Sequential Bayesian Updating</i>. 2019.","chicago":"Hernández Rodriguez, Tanja, C. Posch, J. Schmutzhard, J. Stettner, J. Weihs, R. Pörtner, and Björn Frahm. <i>Predicting Industrial Scale Cell Culture Seed Trains – Considerations of Input Uncertainty, New Process Data and Prognostic Intervals Using a Bayesian Approach and Sequential Bayesian Updating</i>, 2019.","short":"T. Hernández Rodriguez, C. Posch, J. Schmutzhard, J. Stettner, J. Weihs, R. Pörtner, B. Frahm, Predicting Industrial Scale Cell Culture Seed Trains – Considerations of Input Uncertainty, New Process Data and Prognostic Intervals Using a Bayesian Approach and Sequential Bayesian Updating, 2019.","ufg":"<b>Hernández Rodriguez, Tanja et. al. (2019)</b>: Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating.","chicago-de":"Hernández Rodriguez, Tanja, C. Posch, J. Schmutzhard, J. Stettner, J. Weihs, R. Pörtner und Björn Frahm. 2019. <i>Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating</i>."},"year":2019,"user_id":"45666","department":[{"_id":"DEP4021"}],"author":[{"first_name":"Tanja","id":"52466","last_name":"Hernández Rodriguez","full_name":"Hernández Rodriguez, Tanja"},{"first_name":"C.","full_name":"Posch, C.","last_name":"Posch"},{"first_name":"J.","full_name":"Schmutzhard, J.","last_name":"Schmutzhard"},{"first_name":"J.","full_name":"Stettner, J.","last_name":"Stettner"},{"first_name":"J.","full_name":"Weihs, J.","last_name":"Weihs"},{"full_name":"Pörtner, R.","last_name":"Pörtner","first_name":"R."},{"id":"45666","first_name":"Björn","last_name":"Frahm","full_name":"Frahm, Björn"}],"language":[{"iso":"eng"}],"conference":{"location":"Hamburg, Germany","name":"DECHEMA Himmelfahrtstagung “Intensification and digitalisation for integral bioprocessing”","end_date":"2019-05-29","start_date":"2019-05-27"},"status":"public","_id":"3005","date_updated":"2023-03-15T13:49:44Z","date_created":"2020-06-23T13:01:27Z","publication_status":"published"}