Reading List
- Siddiqi, S., Boots, B. and Gordon, G. (2010). Reduced Rank Hidden Markov Models
- Ghahramani, Z. and Hinton, G. (2000). Variational learning for switching-state models
- Singh, S., James, M. and Rudary, M. (2004). Predictive state representations: A new theory for modeling dynamical systems.
- Jaeger, H., Zhao, M., Kretzschmar, K., Oberstein, T., Popovici, D., & Kolling, A. (2006). Learning observable operator models via the es algorithm
- Anandkumar, A., Hsu, D. and Kakade, S. M. (2012). A Method of Moments for Mixture Models and Hidden Markov Models.
- Hsu, D., Kakade, S. M. and Zhang, T. (2009). A Spectral Algorithm for Learning Hidden Markov Models.
- Ninness, B. and Gibson, S. On the relationship between State-Space-Subspace-Based and Maximum-Likelihood System Identification Methods
- Saria, S., Duchi, A. and Koller, D. (2011). Discovering Deformable Motifs in Continuous Time Series data.
- Saria, S., Nodelman, U. and Koller, D. (2007). Reasoning at the Right Time Granularity
- Hao, Y., Chen, Y., Zakaria, J., Hu, B., Rakthanmanon, T. and Keogh, E. (2013). Towards Never-Ending Learning from Time Series Streams?
- Listgarten, J., Neal, R. M., Roweis, S. T. and Emili, A. (2004). Multiple Alignment of Continuous Time Series
Datasets