moment based variational inference for markov jump
play

Moment-Based Variational Inference for Markov Jump Processes - PowerPoint PPT Presentation

Moment-Based Variational Inference for Markov Jump Processes Christian Wildner and Heinz Koeppl Department of Electrical Engineering and Information Technology Technische Universitt Darmstadt, Germany June 11, 2019 | Moment-Based


  1. Moment-Based Variational Inference for Markov Jump Processes Christian Wildner and Heinz Koeppl Department of Electrical Engineering and Information Technology Technische Universität Darmstadt, Germany June 11, 2019 | Moment-Based Variational Inference for Markov Jump Processes | Christian Wildner, Heinz Koeppl | 1

  2. Introduction Model Class: Markov jump process / continuous time Markov chain • Applications in many domains (finance, social networks, healthcare, systems biology, etc.) • Data-driven modelling requires latent state estimation June 11, 2019 | Moment-Based Variational Inference for Markov Jump Processes | Christian Wildner, Heinz Koeppl | 2

  3. Introduction Model Class: Markov jump process / continuous time Markov chain • Applications in many domains (finance, social networks, healthcare, systems biology, etc.) • Data-driven modelling requires latent state estimation Problem: Hard/intractable for large state spaces June 11, 2019 | Moment-Based Variational Inference for Markov Jump Processes | Christian Wildner, Heinz Koeppl | 3

  4. Introduction Model Class: Markov jump process / continuous time Markov chain • Applications in many domains (finance, social networks, healthcare, systems biology, etc.) • Data-driven modelling requires latent state estimation Problem: Hard/intractable for large state spaces Proposed solution: new variational inference approach based on • transition space partitioning • gradient-based optimization June 11, 2019 | Moment-Based Variational Inference for Markov Jump Processes | Christian Wildner, Heinz Koeppl | 4

  5. Markov Jump Processes An MJP is fully defined by • an initial distribution • a transition function with June 11, 2019 | Moment-Based Variational Inference for Markov Jump Processes | Christian Wildner, Heinz Koeppl | 5

  6. Markov Jump Processes An MJP is fully defined by • an initial distribution • a transition function with June 11, 2019 | Moment-Based Variational Inference for Markov Jump Processes | Christian Wildner, Heinz Koeppl | 6

  7. <latexit sha1_base64="LYecnUJ+2k+l/9JOByuW6hbOWik=">ACeHicfVFNSwMxE3X7/Vbj16CpaiXslsLeix48VjB1oJdSjadtdFksySzQln6H7zqP/OveDL9QNwKDgQe781k3szEmRQWg+Cz4q2srq1vbG752zu7e/sHh0dq3PDocO1KYXMwtSpNBgRJ6mQGmYgkP8cvNVH94BWOFTu9xnEGk2FMqEsEZOqrbO8dB42JwUA3qwSzoXxAuQLVF5tEeHFb6/aHmuYIUuWTWPoZBhlHBDAouYeL3cwsZ4y/sCR4dTJkCGxUzuxNac8yQJtq4lyKdsb8rCqasHavYZSqGI7usTckfrVZqhcl1VIg0yxFSPu+U5JKiptPh6VAY4CjHDjBuhDNL+YgZxtGtyPdrlOcWtfr/05JaTF2g1tKWhx6BfAW3EZvM3JY0y5mEYVTMc0paEcdq4rt7hMvb/wu6jXp4W/cNaut5uIym+SEnJzEpIr0iK3pE06hJNn8kbeyUfly6PemXcxT/Uqi5pjUgqv8Q26icHX</latexit> <latexit sha1_base64="yliyfy+gVJBkdxTS+u+7TovaQTk=">ACenicfVFNSwMxE3X7/Vbj15CS0ERym4t6LHgxaOCtYV2Kdl01gaTzZLMCmXpn/Cqf8z/4sH0A3EtOB4vDeTeTMTZ1JYDILPire2vrG5tb3j7+7tHxweHZ8WZ0bDh2upTa9mFmQIoUOCpTQywFUvoxi+3M737CsYKnT7iJINIsedUJIzdFSvd47D5uX4YnhUCxrBPOgqCJeg1iaLuB8eVwaDkea5ghS5ZNb2wyDqGAGBZcw9Qe5hYzxF/YMfQdTpsBGxdzwlNYdM6KJNu6lSOfs74qCKWsnKnaZiuHY/tVm5I9WL7XC5CYqRJrlClfdEpySVHT2fh0JAxwlBMHGDfCmaV8zAzj6Jbk+3XKc4ta/f9pS1mLlBractDj0G+gtuITeZuS5rlTMIoKhY5Ja2IYzX13T3Cv9tfBU/NRnjVaD60au3W8jLb5IxUyTkJyTVpkztyTzqE0neyDv5qHx5Ve/Cu1ykepVlzSkphdf6BjgAwn4=</latexit> <latexit sha1_base64="YamRpAPqzTl/wPlx69cAdPmack=">ACeHicfVFNSwMxE3X7/Vbj16CpaiXslsLeix48VjB1oJdSjadtdFksySzQln6H7zqP/OveDL9QNwKDgQe781k3szEmRQWg+Cz4q2srq1vbG752zu7e/sHh0dq3PDocO1KYXMwtSpNBgRJ6mQGmYgkP8cvNVH94BWOFTu9xnEGk2FMqEsEZOqrbO8dBeDE4qAb1YBb0LwgXoNoi82gPDiv9/lDzXEGKXDJrH8Mgw6hgBgWXMPH7uYWM8Rf2BI8OpkyBjYqZ3QmtOWZIE23cS5HO2N8VBVPWjlXsMhXDkV3WpuSPViu1wuQ6KkSa5Qgpn3dKcklR0+nwdCgMcJRjBxg3wpmlfMQM4+hW5Ps1ynOLWv3/aUktpi5Qa2nLQ49AvoLbiE1mbkua5UzCMCrmOSWtiGM18d09wuXt/wXdRj28rDfumtVWc3GZTXJCTsk5CckVaZFb0iYdwskzeSPv5KPy5VHvzLuYp3qVRc0xKYX+Aa4ecHW</latexit> <latexit sha1_base64="6vCvLUNiKi5Z9kJvpabKuQatLpk=">ACenicfVFNSwMxE3X7/Wr6tFLsBQUoezWgh4LXjwqWFtol5JNZ20w2SzJbKEs/RNe9Y/5XzyYfiCugOBx3szmTczcSaFxSD4qHhr6xubW9s7/u7e/sFh9ej4yercOhwLbXpxcyCFCl0UKCEXmaAqVhCN365nevdCRgrdPqI0wixZ5TkQjO0FG93jkOw8vxbBaCxrBIuhfEK5ArU2WcT8qgwGI81zBSlyazth0GUcEMCi5h5g9yCxnjL+wZ+g6mTIGNioXhGa07ZkQTbdxLkS7YnxUFU9ZOVewyFcOx/a3NyW+tXmqFyU1UiDTLEVK+7JTkqKm8/HpSBjgKcOMG6EM0v5mBnG0S3J9+uU5xa1+v/TklrMXaDW0paHoOcgNuITRZuS5rlTMIoKpY5Ja2IYzXz3T3C39v/C56ajfCq0Xxo1dqt1W2ySk5I+ckJNekTe7IPekQTiR5JW/kvfLpnXkX3uUy1ausak5IKbzWFzXuwn0=</latexit> <latexit sha1_base64="PVtIj65BL5r68OwnfxBdsZ3WYG0=">ACe3icfVHbSgMxE3X+3rXR1+CpeCNslsVfSz4qOC1YpdSjadtaHJZklmhbL0K3zVD/NjBNML4io4EDicM5M5MxNnUlgMgo+KNze/sLi0vOKvrq1vbG5t79xbnRsOLa6lNu2YWZAihRYKlNDODAVS3iIB1dj/eEFjBU6vcNhBpFiz6lIBGfoqMf2AXbD40b/sLtVDerBJOhfEM5AtUmcdPdrnQ6Pc1zBSlyax9CoMo4IZFzCyO/kFjLGB+wZnhxMmQIbFRPHI1pzTI8m2riXIp2wPysKpqwdqthlKoZ9+1sbk9ardQKk8uoEGmWI6R82inJUVNx/PTnjDAUQ4dYNwIZ5byPjOMo9uS79cozy1q9f+nJbUYu0CtpS0P3Qf5Am4jNpm4LWmWMwm9qJjmlLQijtXId/cIf2/L7hv1MPTeuP2rNo8m1meyRfXJAQnJBmuSa3JAW4USRV/JG3iufXtU78k6mqV5lVrNLSuGdfwHEbMK5</latexit> <latexit sha1_base64="t/+ZdG62EhV5OzgpVxhcTH6eCU=">ACeHicfVFNSwMxE3Xr7p+69FLsBT1UnaroMeCF09SwdaCXUo2nbXRZLMks0JZ+h+86j/zr3gy/UBcBQcCj/dmMm9m4kwKi0HwUfGWldW16r/sbm1vbO7t5+1+rcOhwLbXpxcyCFCl0UKCEXmaAqVjCfx8NdXvX8BYodM7HGcQKfaYikRwho7q9k5wcHM62K0FjWAW9C8IF6DWIvNoD/Yq/f5Q81xBilwyax/CIMOoYAYFlzDx+7mFjPFn9gPDqZMgY2Kmd0JrTtmSBNt3EuRztifFQVT1o5V7DIVw5H9rU3Jb61eaoXJZVSINMsRUj7vlOSoqbT4elQGOAoxw4wboQzS/mIGcbRrcj365TnFrX6/9OSWkxdoNbSlocegXwBtxGbzNyWNMuZhGFUzHNKWhHauK7e4S/t/8XdJuN8KzRvD2vtc4Xl6mSQ3JETkhILkiLXJM26RBOnsgreSPvlU+Pesfe6TzVqyxqDkgpvOYX9EnB8w=</latexit> Markov Jump Processes An MJP is fully defined by • an initial distribution • a transition function with X ( t 1 ) X ( t 1 + h ) X ( t 1 + 2 h ) X ( t 2 ) X ( t 2 + h ) X ( t N ) · · · · · · discretized representation of hidden MJP June 11, 2019 | Moment-Based Variational Inference for Markov Jump Processes | Christian Wildner, Heinz Koeppl | 7

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend