102,589 research outputs found
ï»żAn Answer Explanation Model for Probabilistic Database Queries
Following the availability of huge amounts of uncertain data, coming from diverse ranges of applications such as sensors, machine learning or mining approaches, information extraction and integration, etc. in recent years, we have seen a revival of interests in probabilistic databases. Queries over these databases result in probabilistic answers. As the process of arriving at these answers is based on the underlying stored uncertain data, we argue that from the standpoint of an end user, it is helpful for such a system to give an explanation on how it arrives at an answer and on which uncertainty assumptions the derived answer is based. In this way, the user with his/her own knowledge can decide how much confidence to place in this probabilistic answer. \ud
The aim of this paper is to design such an answer explanation model for probabilistic database queries. We report our design principles and show the methods to compute the answer explanations. One of the main contributions of our model is that it fills the gap between giving only the answer probability, and giving the full derivation. Furthermore, we show how to balance verifiability and influence of explanation components through the concept of verifiable views. The behavior of the model and its computational efficiency are demonstrated through an extensive performance study
Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations
Convection initiation (CI) nowcasting remains a challenging problem for both
numerical weather prediction models and existing nowcasting algorithms. In this
study, object-based probabilistic deep learning models are developed to predict
CI based on multichannel infrared GOES-R satellite observations. The data come
from patches surrounding potential CI events identified in Multi-Radar
Multi-Sensor Doppler weather radar products over the Great Plains region from
June and July 2020 and June 2021. An objective radar-based approach is used to
identify these events. The deep learning models significantly outperform the
classical logistic model at lead times up to 1 hour, especially on the false
alarm ratio. Through case studies, the deep learning model exhibits the
dependence on the characteristics of clouds and moisture at multiple levels.
Model explanation further reveals the model's decision-making process with
different baselines. The explanation results highlight the importance of
moisture and cloud features at different levels depending on the choice of
baseline. Our study demonstrates the advantage of using different baselines in
further understanding model behavior and gaining scientific insights
On the Implementation of the Probabilistic Logic Programming Language ProbLog
The past few years have seen a surge of interest in the field of
probabilistic logic learning and statistical relational learning. In this
endeavor, many probabilistic logics have been developed. ProbLog is a recent
probabilistic extension of Prolog motivated by the mining of large biological
networks. In ProbLog, facts can be labeled with probabilities. These facts are
treated as mutually independent random variables that indicate whether these
facts belong to a randomly sampled program. Different kinds of queries can be
posed to ProbLog programs. We introduce algorithms that allow the efficient
execution of these queries, discuss their implementation on top of the
YAP-Prolog system, and evaluate their performance in the context of large
networks of biological entities.Comment: 28 pages; To appear in Theory and Practice of Logic Programming
(TPLP
How could a rational analysis model explain?
Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances
DNF Sampling for ProbLog Inference
Inference in probabilistic logic languages such as ProbLog, an extension of
Prolog with probabilistic facts, is often based on a reduction to a
propositional formula in DNF. Calculating the probability of such a formula
involves the disjoint-sum-problem, which is computationally hard. In this work
we introduce a new approximation method for ProbLog inference which exploits
the DNF to focus sampling. While this DNF sampling technique has been applied
to a variety of tasks before, to the best of our knowledge it has not been used
for inference in probabilistic logic systems. The paper also presents an
experimental comparison with another sampling based inference method previously
introduced for ProbLog.Comment: Online proceedings of the Joint Workshop on Implementation of
Constraint Logic Programming Systems and Logic-based Methods in Programming
Environments (CICLOPS-WLPE 2010), Edinburgh, Scotland, U.K., July 15, 201
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