Search results for "Conditional"

Extending graphical models for applications: on covariates, missingness and normality

2021

The authors of the paper “Bayesian Graphical Models for Modern Biological Applications” have put forward an important framework for making graphical models more useful in applied settings. In this discussion paper, we give a number of suggestions for making this framework even more suitable for practical scenarios. Firstly, we show that an alternative and simplified definition of covariate might make the framework more manageable in high-dimensional settings. Secondly, we point out that the inclusion of missing variables is important for practical data analysis. Finally, we comment on the effect that the Gaussianity assumption has in identifying the underlying conditional independence graph…

On the quantitative edge of feature and gestalt -based associative learning : learning-related ERP changes in the hippocampus and the prefontal corte…

2004

Algebraic aspects and coherence conditions for conjunctions among conditional events

2018

We deepen the study of a notion of conjunction among conditional events, introduced in previous papers in theframework of coherence. This notion of conjunction, differently from other approaches, is given in the setting ofconditional random quantities. We show that some well known properties which are satisfied by conjunctionsof unconditional events are also satisfied by conjunctions of conditional events. In particular we examine anadditive property and a decomposition formula, by also obtaining a generalized inclusion-exclusion formula. Then,by exploiting the notion of conjunction, we introduce the set of constituents generated bynconditional events.Moreover, under logical independence, w…

Compound conditionals as random quantities and Boolean algebras

2022

Conditionals play a key role in different areas of logic and probabilistic reasoning, and they have been studied and formalised from different angles. In this paper we focus on the de Finetti's notion of conditional as a three-valued object, with betting-based semantics, and its related approach as random quantity as mainly developed by two of the authors. Compound conditionals have been studied in the literature, but not in full generality. In this paper we provide a natural procedure to explicitly attach conditional random quantities to arbitrary compound conditionals that also allows us to compute their previsions. By studying the properties of these random quantities, we show that, in f…

Causal Inference in Geoscience and Remote Sensing From Observational Data

2020

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, …

Probabilistic semantics for categorical syllogisms of Figure II

2018

A coherence-based probability semantics for categorical syllogisms of Figure I, which have transitive structures, has been proposed recently (Gilio, Pfeifer, & Sanfilippo [15]). We extend this work by studying Figure II under coherence. Camestres is an example of a Figure II syllogism: from Every P is M and No S is M infer No S is P. We interpret these sentences by suitable conditional probability assessments. Since the probabilistic inference of \(\bar{P}|S\) from the premise set \(\{M|P,\bar{M}|S\}\) is not informative, we add \(p(S|(S \vee P))>0\) as a probabilistic constraint (i.e., an “existential import assumption”) to obtain probabilistic informativeness. We show how to propagate the…

Firm Size and Volatility Analysis in the Spanish Stock Market

2011

In this article, three strongly related questions are studied. First, volatility spillovers between large and small firms in the Spanish stock market are analyzed by using a conditional CAPM with an asymmetric multivariate GARCH-M covariance structure. Results show that there exist bidirectional volatility spillovers between both types of firms, especially after bad news. Second, the volatility feedback hypothesis explaining the volatility asymmetry feature is investigated. Results show significant evidence for this hypothesis. Finally, the study uncovers that conditional beta coefficient estimates within the used model are insensitive to sign and size asymmetries in the unexpected shock re…

Conditional transgenic mouse models: from the basics to genome-wide sets of knockouts and current studies of tissue regeneration

2008

Many mouse models are currently available, providing avenues to elucidate gene function and to recapitulate specific pathological conditions. To a large extent, successful translation of clinical evidence or analytical data into appropriate mouse models is possible through progress in transgenic or gene-targeting technology. Beginning with a review of standard mouse transgenics and conventional gene targeting, this article will move on to discussing the basics of conditional gene expression: the tetracycline (tet)-off and tet-on systems based on the transactivators tet-controlled transactivator (Tta) and reverse tet-on transactivator (rtTA) that allow downregulation or induction of gene exp…

Multiscale Information Storage of Linear Long-Range Correlated Stochastic Processes

2019

Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then …

A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics

2006

We propose a new distributed image segmentation algorithm structured as a multiagent system composed of a set of segmentation agents and a coordinator agent. Starting from its own initial image, each segmentation agent performs the iterated conditional modes method, known as ICM, in applications based on Markov random fields, to obtain a sub-optimal segmented image. The coordinator agent diversifies the initial images using the genetic crossover and mutation operators along with the extremal optimization local search. This combination increases the efficiency of our algorithm and ensures its convergence to an optimal segmentation as it is shown through some experimental results.