A common assumption made in studies involving two or more binary diagnostic tests in the absence of a gold standard is one of conditional independence among tests given disease status. Although reasonable in some cases, often this assumption is untenable or untested and may lead to biased results. We propose a class of hierarchical models for the purpose of estimating the herd-level prevalence ...

Our objective is to study the contribution of naive increased Bayesian networks in problems of image classification. The images used in this study represent the structure of a document containing text blocks and graphics. We proposed three variants of Bayesian networks. First naive Bayesian networks RN who, despite their simple structure and strong assumption on independence have given very goo...

Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption The assumption that the assignment to treatments is ignorable conditional on attributes plays an important role in the applied statistic and econometric evaluation literature. Another term for it is conditional independence assumption. This paper discusses identification when the...

The causal impact of an observable cause can only be estimated if assumptions are made about the presence and impact of possible additional unobservable causes. Current theories of causal reasoning make different assumptions about hidden causes. Some views assume that hidden causes are always present, others that they are independent of the observed causes. In two experiments we assessed people...

• The assumption of independence of errors, i.e., error of an observation is not related to or depends upon that of another. This assumption is usually assured with the use of proper randomization procedure. However, if there is any systematic pattern in the arrangement of treatments from one replication to another, errors may be nonindependent. • The assumption of additive effects can be defin...

Cellular automata are discrete agent-based models, generally used in cell-based applications. There is much interest in obtaining continuum models that describe the mean behaviour of the agents in these models. Previously, continuum models have been derived for agents undergoing motility and proliferation processes, however, these models only hold under restricted conditions. In order to narrow...

This paper considers the problem of combining belief functions obtained from not necessarily independent sources of information. It introduces two combination rules for the situation in which no assumption is made about the dependence of the information sources. These two rules are based on cautious combinations of plausibility and commonality functions, respectively. The paper studies the prop...

The assumption that the assignment to treatments is ignorable conditional on attributes plays an important role in the applied statistic and econometric evaluation literature (Conditional Independence Assumption). This paper discusses identification using CIA when there are more than two types of mutually exclusive treatments. It turns out that low dimensional balancing scores, similar to the o...

Summary There are many kinds of exogeneity assumptions. How should researchers choose among them? When is imposed on an unobservable like a potential outcome, we argue that the form be chosen based kind selection unobservables it allows. Consequently, can assess plausibility any assumption by studying distributions treatment given consistent with assumption. We use this approach to study two co...

This paper investigates the formalization of independence in logic, in particular between quantifiers. We first illustrate that this form of independence plays a role in many fields. A logic that has the possibility to express such a form of independence is Independence Friendly logic (introduced by Hintikka); its semantics is defined by means of strategies in a sequential game. It will be show...