28,045 research outputs found
Multiple Quantitative Trait Analysis Using Bayesian Networks
Models for genome-wide prediction and association studies usually target a
single phenotypic trait. However, in animal and plant genetics it is common to
record information on multiple phenotypes for each individual that will be
genotyped. Modeling traits individually disregards the fact that they are most
likely associated due to pleiotropy and shared biological basis, thus providing
only a partial, confounded view of genetic effects and phenotypic interactions.
In this paper we use data from a Multiparent Advanced Generation Inter-Cross
(MAGIC) winter wheat population to explore Bayesian networks as a convenient
and interpretable framework for the simultaneous modeling of multiple
quantitative traits. We show that they are equivalent to multivariate genetic
best linear unbiased prediction (GBLUP), and that they are competitive with
single-trait elastic net and single-trait GBLUP in predictive performance.
Finally, we discuss their relationship with other additive-effects models and
their advantages in inference and interpretation. MAGIC populations provide an
ideal setting for this kind of investigation because the very low population
structure and large sample size result in predictive models with good power and
limited confounding due to relatedness.Comment: 28 pages, 1 figure, code at
http://www.bnlearn.com/research/genetics1
Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies
Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.Adaptation to Climate Change, Bayesian Network, Uncertainty
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
DADA: data assimilation for the detection and attribution of weather and climate-related events
A new nudging method for data assimilation, delayâcoordinate nudging, is presented. Delayâcoordinate nudging makes explicit use of present and past observations in the formulation of the forcing driving the model evolution at each time step. Numerical experiments with a lowâorder chaotic system show that the new method systematically outperforms standard nudging in different model and observational scenarios, also when using an unoptimized formulation of the delayânudging coefficients. A connection between the optimal delay and the dominant Lyapunov exponent of the dynamics is found based on heuristic arguments and is confirmed by the numerical results, providing a guideline for the practical implementation of the algorithm. Delayâcoordinate nudging preserves the easiness of implementation, the intuitive functioning and the reduced computational cost of the standard nudging, making it a potential alternative especially in the field of seasonalâtoâdecadal predictions with large Earth system models that limit the use of more sophisticated data assimilation procedures
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
While the success of semi-supervised learning (SSL) is still not fully
understood, Sch\"olkopf et al. (2012) have established a link to the principle
of independent causal mechanisms. They conclude that SSL should be impossible
when predicting a target variable from its causes, but possible when predicting
it from its effects. Since both these cases are somewhat restrictive, we extend
their work by considering classification using cause and effect features at the
same time, such as predicting disease from both risk factors and symptoms.
While standard SSL exploits information contained in the marginal distribution
of all inputs (to improve the estimate of the conditional distribution of the
target given inputs), we argue that in our more general setting we should use
information in the conditional distribution of effect features given causal
features. We explore how this insight generalises the previous understanding,
and how it relates to and can be exploited algorithmically for SSL.Comment: 36th Conference on Uncertainty in Artificial Intelligence (2020)
(Previously presented at the NeurIPS 2019 workshop "Do the right thing":
machine learning and causal inference for improved decision making,
Vancouver, Canada.
- âŠ