Continuous Graphical Models for Static and Dynamic Distributions: Application to Structural Biology
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Summary
This thesis develops new and improved continuous graphical models, to be used in modeling of protein structure, and develops consistent and efficient algorithms for sparse structure learning and parameter estimation, and inference.
- Type
- article
- Published
- 2013-01-01
- Cited by
- 2
- References
- 104
- Access
- Open access
- OpenAlex
- https://openalex.org/W33707932
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11583798
Keywords
Graphical model, Computer science, Inference, Machine learning, Artificial intelligence
References
- Particle Belief Propagation
- Time-Varying Gaussian Graphical Models of Molecular Dynamics Data
- Making Large-Scale Nyström Approximation Possible
- Learning Sparse Markov Network Structure via Ensemble-of-Trees Models
- Nonparametric Tree Graphical Models
- From Zero to Reproducing Kernel Hilbert Spaces in Twelve Pages or Less
- Blow-up of semilinear PDE's at the critical dimension. A probabilistic approach
- Expectation Propagation for approximate Bayesian inference
- Estimating time-varying networks
- On the shortest spanning subtree of a graph and the traveling salesman problem
- Improved Nyström low-rank approximation and error analysis
- On Estimating Regression
- Time varying undirected graphs
- Catalysis of cis/trans isomerization in native HIV-1 capsid by human cyclophilin A
- The denatured state of Engrailed Homeodomain under denaturing and native conditions.
- Collective protein dynamics in relation to function.
- Statistical Analysis of Circular Data
- High-dimensional graphs and variable selection with the Lasso
- Support vector machines
- A Bayesian statistics approach to multiscale coarse graining.
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