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Found 29 papers, 10 papers with code
Conservative Inference Rule for Uncertain Reasoning under Incompleteness
In this paper we formulate the problem of inference under incomplete information in very general terms.
Updating with incomplete observations
This is a fundamental problem, and of particular interest for Bayesian networks.
Robust Feature Selection by Mutual Information Distributions
Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables.
Desirability and the birth of incomplete preferences
On this basis, we obtain new results and insights: in particular, we show that the theory of incomplete preferences can be developed assuming only the existence of a worst act---no best act is needed---, and that a weakened Archimedean axiom suffices too; this axiom allows us also to address some controversy about the regularity assumption (that probabilities should be positive---they need not), which enables us also to deal with uncountable possibility spaces; we show that it is always possible to extend in a minimal way a preference relation to one with a worst act, and yet the resulting relation is never Archimedean, except in a trivial case; we show that the traditional notion of state independence coincides with the notion called strong independence in imprecise probability---this leads us to give much a weaker definition of state independence than the traditional one; we rework and uniform the notions of complete preferences, beliefs, values; we argue that Archimedeanity does not capture all the problems that can be modelled with sets of expected utilities and we provide a new notion that does precisely that.
Learning Bayesian Networks with Thousands of Variables
We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints.
State Space representation of non-stationary Gaussian Processes
The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest.
Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results.
Statistical comparison of classifiers through Bayesian hierarchical modelling
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst).
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
Entropy-based Pruning for Learning Bayesian Networks using BIC
For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score.
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables.
Recursive Estimation for Sparse Gaussian Process Regression
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications.
Orthogonally Decoupled Variational Fourier Features
Sparse inducing points have long been a standard method to fit Gaussian processes to big data.
Structural Causal Models Are (Solvable by) Credal Networks
A structural causal model is made of endogenous (manifest) and exogenous (latent) variables.
Time series forecasting with Gaussian Processes needs priors
Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention.
Causal Expectation-Maximisation
Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of causation.
Information algebras in the theory of imprecise probabilities
In this paper, we show that coherent sets of gambles and coherent lower and upper previsions can be embedded into the algebraic structure of information algebra.
Information algebras of coherent sets of gambles in general possibility spaces
In this paper, we show that coherent sets of gambles can be embedded into the algebraic structure of information algebra.
Algebras of Sets and Coherent Sets of Gambles
In a recent work we have shown how to construct an information algebra of coherent sets of gambles defined on general possibility spaces.
Correlated Product of Experts for Sparse Gaussian Process Regression
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering.
Bounding Counterfactuals under Selection Bias
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation.
Nonlinear desirability theory
We show how Allais paradox finds a solution in the new theory, and discuss the role of sets of probabilities in the theory.
Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
Efficient Computation of Counterfactual Bounds
This allows us to compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models.
Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
Tractable Bounding of Counterfactual Queries by Knowledge Compilation
We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models.
Zero-shot Causal Graph Extrapolation from Text via LLMs
We evaluate the ability of large language models (LLMs) to infer causal relations from natural language.
A Note on Bayesian Networks with Latent Root Variables
We prove that (i) the likelihood of such a dataset from the original Bayesian network is dominated by the global maximum of the likelihood from the empirical one; and that (ii) such a maximum is attained if and only if the parameters of the Bayesian network are consistent with those of the empirical model.
