Search Results for author:
Found 8 papers, 1 papers with code
The divergence time of protein structures modelled by Markov matrices and its relation to the divergence of sequences
Using these inferred models and the relationship between the divergence of sequences and structures, we demonstrate a competitive performance in secondary structure prediction against neural network architectures commonly employed for this task.
Subclasses of Class Function used to Implement Transformations of Statistical Models
A library of software for inductive inference guided by the Minimum Message Length (MML) principle was created previously.
Causal KL: Evaluating Causal Discovery
The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model.
Markov Blanket Discovery using Minimum Message Length
Causal discovery automates the learning of causal Bayesian networks from data and has been of active interest from their beginning.
Bridging the Gaps in Statistical Models of Protein Alignment
This work demonstrates how a complete statistical model quantifying the evolution of pairs of aligned proteins can be constructed from a time-parameterised substitution matrix and a time-parameterised 3-state alignment machine.
Proving the NP-completeness of optimal moral graph triangulation
Moral graphs were introduced in the 1980s as an intermediate step when transforming a Bayesian network to a junction tree, on which exact belief propagation can be efficiently done.
The Complexity of Morality: Checking Markov Blanket Consistency with DAGs via Morality
A family of Markov blankets in a faithful Bayesian network satisfies the symmetry and consistency properties.
Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions
The key contributions of this paper, in addition to the general search and inference methodology, include the derivation of MML expressions for encoding the data using multivariate Gaussian and von Mises-Fisher distributions, and the analytical derivation of the MML estimates of the parameters of the two distributions.
