Academic literature on the topic 'Cautious inference'
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Journal articles on the topic "Cautious inference"
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Thöne, Helmut, Werner Kießling, and Ulrich Güntzer. "On cautious probabilistic inference and default detachment." Annals of Operations Research 55, no. 1 (February 1995): 195–224. http://dx.doi.org/10.1007/bf02031721.
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Dafoe, Allan, John R. Oneal, and Bruce Russett. "The Democratic Peace: Weighing the Evidence and Cautious Inference." International Studies Quarterly 57, no. 1 (March 2013): 201–14. http://dx.doi.org/10.1111/isqu.12055.
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De Cooman, Gert, Jasper De Bock, and Márcio Alves Diniz. "Coherent Predictive Inference under Exchangeability with Imprecise Probabilities." Journal of Artificial Intelligence Research 52 (January 10, 2015): 1–95. http://dx.doi.org/10.1613/jair.4490.
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Coherent reasoning under uncertainty can be represented in a very general manner by coherent sets of desirable gambles. In a context that does not allow for indecision, this leads to an approach that is mathematically equivalent to working with coherent conditional probabilities. If we do allow for indecision, this leads to a more general foundation for coherent (imprecise-)probabilistic inference. In this framework, and for a given finite category set, coherent predictive inference under exchangeability can be represented using Bernstein coherent cones of multivariate polynomials on the simplex generated by this category set. This is a powerful generalisation of de Finetti's Representation Theorem allowing for both imprecision and indecision. We define an inference system as a map that associates a Bernstein coherent cone of polynomials with every finite category set. Many inference principles encountered in the literature can then be interpreted, and represented mathematically, as restrictions on such maps. We discuss, as particular examples, two important inference principles: representation insensitivitya strengthened version of Walley's representation invarianceand specificity. We show that there is an infinity of inference systems that satisfy these two principles, amongst which we discuss in particular the skeptically cautious inference system, the inference systems corresponding to (a modified version of) Walley and Bernard's Imprecise Dirichlet Multinomial Models (IDMM), the skeptical IDMM inference systems, and the Haldane inference system. We also prove that the latter produces the same posterior inferences as would be obtained using Haldane's improper prior, implying that there is an infinity of proper priors that produce the same coherent posterior inferences as Haldane's improper one. Finally, we impose an additional inference principle that allows us to characterise uniquely the immediate predictions for the IDMM inference systems.
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Rogers, Mark F., Colin Campbell, and Yiming Ying. "Probabilistic Inference of Biological Networks via Data Integration." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/707453.
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There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations. By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances. Although one pairwise kernel (the tensor product pairwise kernel) appears to work best, different kernels may contribute complimentary information about interactions: experiments inS. cerevisiae(yeast) reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy. Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.
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Lee, Jinhee, and Inseok Song. "Effect of Prior Information on Bayesian Membership Calculations for Nearby Young Star Associations." Proceedings of the International Astronomical Union 10, S314 (November 2015): 67–68. http://dx.doi.org/10.1017/s1743921315006341.
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AbstractWe present a refined moving group membership diagnostics scheme based on Bayesian inference. Compared to the BANYAN II method, we improved the calculation by updating bona fide members of a moving group, field star treatment, and uniform spatial distribution of moving group members. Here, we present the detailed description of our method and the new results for Bayesian membership calculation. Comparison of our method with BANYAN II shows probability differences up to ~90%. We conclude that more cautious consideration is needed in moving group membership based on Bayesian inference.
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TRILLAS, E. "ON LOGIC AND FUZZY LOGIC." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 01, no. 02 (December 1993): 107–37. http://dx.doi.org/10.1142/s0218488593000073.
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This paper mainly consists of a review of some basic tools of Inexact Inference, its reduction to classical logic and its cautious use of Fuzzy Logic. Those tools are the concept of Conditional Relation, its greatest case of Material Conditional and the concept of Logical-States as possible worlds of "true" elements. Some recent results characterizing Monotonic Preorders are also introduced, in both the Classical and Fuzzy cases. Everything lies on the semantic level of Logic and is presented in a naive mathematical style.
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O’Neil-Pirozzi, Therese M. "Cautious inference of support for thickening liquids for persons with dementia in residential aged care facilities." Evidence-Based Communication Assessment and Intervention 7, no. 4 (December 2013): 135–38. http://dx.doi.org/10.1080/17489539.2014.923175.
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Wang, Liang-Jong, Yen-Wei Chou, and Jen-Pan Huang. "Testing the Effect of Sampling Effort on Inferring Phylogeographic History in Psolodesmus mandarinus (Calopterygidae, Odonata)." Diversity 14, no. 10 (September 28, 2022): 809. http://dx.doi.org/10.3390/d14100809.
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Phylogeographic studies have revealed spatial genetic structure and inferred geographical processes that may have generated genetic diversity and divergence. These study results have implications not only on the processes that generate intraspecific and interspecific diversity but also on the essential integrals for defining evolutionary entities (e.g., species). However, the resulting phylogeographic inferences might be impacted by the sampling design, i.e., the number of individuals per population and the number of geographic populations studied. The effect of sampling bias on phylogeographic inferences remains poorly explored. With a comprehensive sampling design (including 186 samples from 56 localities), we studied the phylogeographic history of a Taiwanese endemic damselfly, Psolodesmus mandarinus, with a specific focus on testing the impact of the sampling design on phylogeographic inference. We found a significant difference in the genetic structure of eastern and western populations separated by the Central Mountain Range (CMR) of Taiwan. However, isolation by the CMR did not lead to reciprocally monophyletic geographic populations. We further showed that, when only a subset of individuals was randomly included in the study, monophyletic geographic populations were obtained. Furthermore, historical demographic expansion could become undetectable when only a subset of samples was used in the analyses. Our results demonstrate the impact of sampling design on phylogeographic inferences. Future studies need to be cautious when inferring the effect of isolation by a physical barrier.
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Kyriakopoulos, Grigorios, Stamatios Ntanos, Theodoros Anagnostopoulos, Nikolaos Tsotsolas, Ioannis Salmon, and Klimis Ntalianis. "Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes." International Journal of Environmental Research and Public Health 17, no. 2 (January 8, 2020): 408. http://dx.doi.org/10.3390/ijerph17020408.
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Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.
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Stumpf, Michael P. H. "Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds." Journal of The Royal Society Interface 17, no. 171 (October 2020): 20200419. http://dx.doi.org/10.1098/rsif.2020.0419.
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Recent progress in theoretical systems biology, applied mathematics and computational statistics allows us to compare the performance of different candidate models at describing a particular biological system quantitatively. Model selection has been applied with great success to problems where a small number—typically less than 10—of models are compared, but recent studies have started to consider thousands and even millions of candidate models. Often, however, we are left with sets of models that are compatible with the data, and then we can use ensembles of models to make predictions. These ensembles can have very desirable characteristics, but as I show here are not guaranteed to improve on individual estimators or predictors. I will show in the cases of model selection and network inference when we can trust ensembles, and when we should be cautious. The analyses suggest that the careful construction of an ensemble—choosing good predictors—is of paramount importance, more than had perhaps been realized before: merely adding different methods does not suffice. The success of ensemble network inference methods is also shown to rest on their ability to suppress false-positive results. A Jupyter notebook which allows carrying out an assessment of ensemble estimators is provided.
Dissertations / Theses on the topic "Cautious inference"
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CAMPAGNER, ANDREA. "Robust Learning Methods for Imprecise Data and Cautious Inference." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404829.
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La rappresentazione, quantificazione e gestione dell'incertezza è uno dei problemi centrali nell'Intelligenza Artificiale, ed in particolare nel Machine Learning, in cui l'incertezza è intrinsecamente collegata alla natura induttiva dell'apprendimento. Tra diverse forme d'incertezza, la modellazione dell'imprecisione, cioè il problem di gestire dati o conoscenza imperfetta o incompleta, ha recentemente attratto molto interesse nella comunità di ricerca, per via delle sue implicazione teoriche e applicate sull'uso di strumenti basati sul Machine Learning.
Questo lavoro si concentra sul problema di gestire l'imprecision nel Machine Learning, sotto due diverse prospettive. Da un lato, l'imprecisione che riguarda i dati di input alla pipeline di Machine Learning, da cui si origina il problema dell'apprendimento da dati imprecisi. Dall'altro, l'imprecisione come strumento per implementare processi di quantificazione dell'incertezza nel Machine Learning, al fine di permettere a questi ultimi di fornire previsioni set-valued e portare quindi alla definizione di metodi di inferenza cauta. Lo scopo di questo lavoro, quindi, riguarda lo studio teorico ed empirico dei due scenari summenzionati.
Per quanto riguarda il problema dell'apprendimento da dati imprecisi, il focus principale riguarda l'investigazione del problema dell'apprendimento da fuzzy label, sia da un punto di visto teorico che algoritmo. I contributi principali includono: la proposta di una caratterizzazione teorica del problema; la proposta di un nuovo algoritmo di ensemble, basato su pseudo-label, e il suo studio dal punto di visto teorico ed empirico; l'applicazione del summenzionato algoritmo in tre problemi medici reali; ed infine la proposta e lo studio di algoritmi di feature selection per ridurre la complessità computazionale e limitare la "curse of dimensionality" per algoritmi di apprendimento da fuzzy label.
Per quanto riguarda l'inferenza cauta, il focus principale riguarda lo studio teorico di tre framework per l'inferenza cauta e lo sviluppo di nuovi algoritmi ed approcci per estendere l'applicabilità di tali framework in setting complessi. I contributi principali in questo senso riguardo lo studio delle proprietà teoriche di, e le relazioni tra, metodi di inferenza cauta decision-teorici, basati sulla selective prediction e sulla conformal prediction; lo studio di modelli ensemble di inferenza cauta, sia da un punto di vista empirico che teorico, mostrando in particolare che tali ensemble permettono di migliorare la robustezza e la generalizzazione di algoritmi di Machine Learning, nonché di facilitare l'applicazione di metodi d'inferenza cauta a dati complessi, multi-sorgenti o multi-modaliThe representation, quantification and proper management of uncertainty is one of the central problems in Artificial Intelligence, and particularly so in Machine Learning, in which uncertainty is intrinsically tied to the inductive nature of the learning problem. Among different forms of uncertainty, the modeling of imprecision, that is the problem of dealing with data or knowledge that are imperfect} and incomplete, has recently attracted interest in the research community, for its theoretical and application-oriented implications on the practice and use of Machine Learning-based tools and methods. This work focuses on the problem of dealing with imprecision in Machine Learning, from two different perspectives. On the one hand, when imprecision affects the input data to a Machine Learning pipeline, leading to the problem of learning from imprecise data. On the other hand, when imprecision is used a way to implement uncertainty quantification for Machine Learning methods, by allowing these latter to provide set-valued predictions, leading to so-called cautious inference methods. The aim of this work, then, will be to investigate theoretical as well as empirical issues related to the two above mentioned settings. Within the context of learning from imprecise data, focus will be given on the investigation of the learning from fuzzy labels setting, both from a learning-theoretical and algorithmic point of view. Main contributions in this sense include: a learning-theoretical characterization of the hardness of learning from fuzzy labels problem; the proposal of a novel, pseudo labels-based, ensemble learning algorithm along with its theoretical study and empirical analysis, by which it is shown to provide promising results in comparison with the state-of-the-art; the application of this latter algorithm in three relevant real-world medical problems, in which imprecision occurs, respectively, due to the presence of conflicting expert opinions, the use of vague technical vocabulary, and the presence of individual variability in biochemical parameters; as well as the proposal of feature selection algorithms that may help in reducing the computational complexity of this task or limit the curse of dimensionality. Within the context of cautious inference, focus will be given to the theoretical study of three popular cautious inference frameworks, as well as to the development of novel algorithms and approaches to further the application of cautious inference in relevant settings. Main contributions in this sense include the study of the theoretical properties of, and relationships among, decision-theoretic, selective prediction and conformal prediction methods; the proposal of novel cautious inference techniques drawing from the interaction between decision-theoretic and conformal predictions methods, and their evaluation in medical settings; as well as the study of ensemble of cautious inference models, both from an empirical point of view, as well as from a theoretical one, by which it is shown that such ensembles could be useful to improve robustness, generalization, as well as to facilitate application of cautious inference methods on multi-source and multi-modal data.
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Wagner, Sander. "Cautious inference : random life course events of parents and children in context." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/277361.
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Esta tesis aborda el tema de los experimentos naturales o políticos que afectan el curso de la vida de padres e hijos y de las limitaciones de inferencia en tales experimentos. El primer trabajo "A non-linear Assessment of Preschool Effects" evalúa de forma no lineal la asignación de diferentes programas pre-escolares para niños daneses. Constatando que los resultados anteriores de encontrar efectos débiles se mantienen, pero que los niños y niñas muestran sorprendentemente diferentes efectos no lineales. El segundo papel "Rusty Instruments?" muestra que el método estándar para estimar los efectos de los niños en los resultados del mercado de trabajo, observando los hermanos gemelos sufre de sesgo, deriva en el diferente comportamiento de la fertilidad posterior de las madres con gemelos y las madres sin gemelos. El último trabajo "“Child Gender and its Effects on Parental Labor Market Participation", prueba que los efectos del sexo del niño sobre la participación de los padres en el mercado de trabajo son robustos para controlar los factores que influyen en el sexo del niño.This thesis deals with natural and policy experiments affecting the life course of parents and children. It also deals with the limitations of using such experiments for causal inference. The first paper “A non-linear Assessment of Preschool Effects” looks at the assignment of different pre-school programs to Danish children. It is found that previous results finding weak effects hold up, but that boys and girls show surprisingly different nonlinear effects. The second paper “Rusty Instruments?” shows that the standard approach to estimating the effects of children on labour market outcomes, by looking at twinning suffers from biases, stemming from different subsequent fertility behaviour of twinning and non-twinnning mothers. The last paper “Child Gender and its Effects on Parental Labor Market Participation” shows that the effects of child gender on parental labour market participation are robust to controlling for factors influencing child gender.
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Plaß, Julia [Verfasser], and Thomas [Akademischer Betreuer] Augustin. "Statistical modelling of categorical data under ontic and epistemic imprecision : contributions to power set based analyses, cautious likelihood inference and (non-)testability of coarsening mechanisms / Julia Plaß ; Betreuer: Thomas Augustin." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2018. http://d-nb.info/116087624X/34.
Full textBooks on the topic "Cautious inference"
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Radden, Jennifer, and Somogy Varga. The Epistemological Value of Depression Memoirs. Edited by K. W. M. Fulford, Martin Davies, Richard G. T. Gipps, George Graham, John Z. Sadler, Giovanni Stanghellini, and Tim Thornton. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199579563.013.0009.
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This chapter argues that despite the recent, welcome interest in autobiographical writing about depression, its use for research purposes presents an epistemological challenge because the extent to which these descriptions illuminate the true nature of depressive experiencecannot be discerned. Contextualized within the genre of autobiography as well as the subgenre of illness memoir (or "autopathography"), the depression memoir exhibits ambiguities, it is shown, imposed by the constraints of its genre, and by the nature of autobiographical memory. Sources of ambiguity distinctive to depression memoirs are next introduced, some tied to cultural meanings, others to the status of depressive states as constituted by moods. Finally, some empirical corroboration for these claims is cited, in findings indicating that depression affects autobiographical memory and writing style. The indeterminacy identified here is not a reason to dismiss depression memoirs, it is concluded, so much as to employ caution in drawing inferences from them.
Book chapters on the topic "Cautious inference"
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Grossmann, Matt. "Reasons for Cautious Optimism." In How Social Science Got Better, 233–56. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197518977.003.0011.
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The explosion of data collection and availability, the expansion of academia and the spread of ideas, and innovations in theory and method all suggest bright days ahead for social science. Addressing human collective challenges such as climate change, poverty, and public health depends on the advance of social science. I revisit the benefits of accounting for human bias in advancing these efforts and for the further understanding of ourselves. I embrace reforms, but as pieces of a pluralist landscape rather than strictures. Descriptive inferences of generalized patterns, causal inference, and qualitative explorations will all remain important to the advance of social knowledge.
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Goldman, Lawrence. "Adolphe Quetelet." In Victorians and Numbers, 139–55. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192847744.003.0007.
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L. A. J. (Adolphe) Quetelet, a Belgian, was the preeminent social statistician in mid-nineteenth century Europe. Widely admired in Britain, where his influence on Florence Nightingale was profound, his friends included William Farr and Charles Babbage, and he had been tutor to Prince Albert. He was present in Cambridge in 1833 when the Statistical Movement was initiated. A mathematician and astronomer who had graduated to the study of social science, his major work Sur L’Homme et les Développement de ses Facultés: Physique Sociale, published in 1835, was a bible for many in Britain. Social Physics combined anthropometry (the study of the dimensions of the human body), demography, and statistical representation of social behaviour. Quetelet was controversial because his ideas veered towards determinism: the regularity of certain social processes led him to argue that men and women had only limited free will and that their actions were dictated by social and institutional contexts. Many Victorians believed strongly in individual moral responsibility, however. His idea of the average man (l’homme moyen) was just as confusing: at times it seemed that Quetelet was describing ideal characteristics rather than average attributes. This chapter attempts to explain his contribution to the British statistical movement, his key ideas, and his errors of reasoning and inference. It compares him to another liberal thinker of this era, the Frenchman Alexis de Tocqueville, who was also interested in statistics, had attended the Manchester Statistical Society, but was a more cautious and sceptical democrat than Quetelet.
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Racine, Jeffrey S. "Random Walks, Unit Roots, and Spurious Relationships." In Reproducible Econometrics Using R, 23–36. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190900663.003.0002.
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This chapter outlines pitfalls of using standard inference procedures common in cross- sectional settings in time series settings and presents alternative procedures. It also addresses the issue of spurious regression and cautions the reader against the unquestioning use of cross section tools in time series settings.
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Schwartz, Sharon, and Nicolle M. Gatto. "What Would Have Been Is Not What Would Be: Counterfactuals of the Past and Potential Outcomes of the Future." In Causality and Psychopathology. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780199754649.003.0006.
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Epidemiology is often described as the basic science of public health. A mainstay of epidemiologic research is to uncover the causes of disease that can serve as the basis for successful public-health interventions (e.g., Institute of Medicine, 1988; Milbank Memorial Fund Commission, 1976). A major obstacle to attaining this goal is that causes can never be seen but only inferred. For this reason, the inferences drawn from our studies must always be interpreted with caution. Considerable progress has been made in the methods required for sound causal inference. Much of this progress is rooted in a full and rich articulation of the logic behind randomized controlled trials (Holland, 1986). From this work, epidemiologists have a much better understanding of barriers to causal inference in observational studies, such as confounding and selection bias, and their tools and concepts are much more refined. The models behind this progress are often referred to as ‘‘counterfactual’’ models. Although researchers may be unfamiliar with them, they are widely (although not universally) accepted in the field. Counterfactual models underlie the methodologies that we all use. Within epidemiology, when people talk about a counterfactual model, they usually mean a potential outcomes model—also known as ‘‘Rubin’s causal model.’’ As laid out by epidemiologists, the potential outcomes model is rooted in the experimental ideas of Cox and Fisher, for which Neyman provided the first mathematical expression. It was popularized by Rubin, who extended it to observational studies, and expanded by Robins to exposures that vary over time (Maldonado & Greenland, 2002; Hernan, 2004; VanderWeele & Hernan, 2006). This rich tradition is responsible for much of the progress we have just noted. Despite this progress in methods of causal inference, a common charge in the epidemiologic literature is that public-health interventions based on the causes we identify in our studies often fail.
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Carr-Hill, Roy. "Using survey data: towards valid estimates of poverty in the South." In Data in Society, 79–90. Policy Press, 2019. http://dx.doi.org/10.1332/policypress/9781447348214.003.0007.
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It is important to be cautious about making inferences from survey data. This chapter focuses on one very important but unexamined problem, that of the undercount of the poorest in the world. This arises both by design (excluding the homeless, those in institutions and nomadic populations) and in practice (those in fragile households, urban slums, insecure areas and servants/slaves in rich households). In developing countries, it is difficult to make inter-censal estimates because essential data like birth and death registration are not systematically collected. Donors have therefore promoted the use of international standardized household surveys. A possible alternative is Citizen surveys initiated by an Indian NGO (Pratham). Comparisons are made between citizen surveys and contemporaneous Demographic and Health Surveys in three East African countries
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Lycett, Stephen J. "Cultural Transmission from the Last Common Ancestor to the Levallois Reducers." In Squeezing Minds From Stones, 251–77. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190854614.003.0013.
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Given the gap between ape and human cultural capacities, the question of what we can infer about evolving cultural capacities during the course of human evolution presents itself. Tom Wynn has stressed the importance of a comparative (cross-species) approach and the idea of inferring only the minimal capacities required to explain archaeological phenomena in cognitive terms. In this chapter, these principles are applied to infer what can reasonably be determined about cultural transmission capacities in extinct hominins from the last common ancestor to the producers of Levallois. Although much remains to be learned, and a provisional model must caution against false negatives and false positive attributions, the approach yields reasonable inferences regarding our evolving cultural capacities over the long stretch of time from the end of the Miocene through to the later Middle Pleistocene. This situation also leads to a position where possible avenues of future enquiry might usefully be identified.
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Allison, Penelope M. "Artefact function and the distribution of activities." In The Insula of the Menander at Pompeii. Oxford University Press, 2007. http://dx.doi.org/10.1093/oso/9780199263127.003.0040.
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In chapter 3, I discussed some of the nomenclature ascribed to Roman artefacts by modern scholars. In certain cases, I assessed the validity of this nomenclature’s assignation of functions to particular artefacts in this catalogue, especially with regard to Latin labels. The present chapter develops this discussion further, by using this contextualized study of finds assemblages to further our knowledge of the activities for which certain types of artefacts were used and of the spatial distribution of these activities. Sometimes the contexts and assemblages support and broaden our current knowledge, but at other times this study throws doubt on our current understandings of the relationships between this material and human activity. There is not space here to deal with all the classes of material in this catalogue so a few examples are highlighted. However, it should be noted that these observations are specific to Pompeii, and that any extrapolation from them to other parts of the Roman Empire, without further evidence, should be treated with caution. This discussion is also based predominantly on artefact distribution in Pompeii and any analogical inference which might be drawn from literary sources is avoided. This does not mean, however, that such inference should not be made using the results. The following labels, many of which were highlighted in the terminology chapter (Chapter 3), give either misleading or non-specific perspectives on the functions of certain Roman artefacts. Examples from this catalogue are used to demonstrate this, and to propose alternative possibilities. This term means a drinking vessel and is used in the Pompeii reports for a small biconical ceramic pot. None of the so-labelled ceramic examples in this catalogue has actually survived. However, a bronze vessel of the form to which this label was given (cat. no. 1286), was found in portico 10 of the Casa del Fabbro, together with a comparable jar (cat. no. 1287), but probably in a salvage assemblage. In the Casa del Menandro, one so-labelled ceramic pot (cat. no. 828) was recorded in the north-east corner of Room 43, together with a number of larger bronze vessels. The latter were possibly associated with ablutions, the serving of food and wine, and religion.
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Shrout, Patrick E. "Integrating Causal Analysis into Psychopathology Research." In Causality and Psychopathology. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780199754649.003.0005.
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Both in psychopathology research and in clinical practice, causal thinking is natural and productive. In the past decades, important progress has been made in the treatment of disorders ranging from attention-deficit/hyperactivity disorder (e.g., Connor, Glatt, Lopez, Jackson, & Melloni, 2002) to depression (e.g., Dobson, 1989; Hansen, Gartlehner, Lohr, Gaynes, & Carey, 2005) to schizophrenia (Hegarty, Baldessarini, Tohen, & Waternaux, 1994). The treatments for these disorders include pharmacological agents as well as behavioral interventions, which have been subjected to clinical trials and other empirical evaluations. Often, the treatments focus on the reduction or elimination of symptoms, but in other cases the interventions are designed to prevent the disorder itself (Brotman et al., 2008). In both instances, the interventions illustrate the best use of causal thinking to advance both scientific theory and clinical practice. When clinicians understand the causal nature of treatments, they can have confidence that their actions will lead to positive outcomes. Moreover, being able to communicate this confidence tends to increase a patient’s comfort and compliance (Becker & Maiman, 1975). Indeed, there seems to be a basic inclination for humans to engage in causal explanation, and such explanations affect both basic thinking, such as identification of categories (Rehder & Kim, 2006), and emotional functioning (Hareli & Hess, 2008). This inclination may lead some to ascribe causal explanations to mere correlations or coincidences, and many scientific texts warn researchers to be cautious about making causal claims (e.g., Maxwell & Delaney, 2004). These warnings have been taken to heart by editors, reviewers, and scientists themselves; and there is often reluctance regarding the use of causal language in the psychopathology literature. As a result, many articles simply report patterns of association and refer to mechanisms with euphemisms that imply causal thinking without addressing causal issues head-on. Over 35 years ago Rubin (1974) began to talk about strong causal inferences that could be made from experimental and nonexperimental studies using the so-called potential outcomes approach. This approach clarified the nature of the effects of causes A vs. B by asking us to consider what would happen to a given subject under these two conditions.
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Dobs, Adrian, and Swaytha Yalamanchi. "Risks of Testosterone Treatment." In Oxford Textbook of Endocrinology and Diabetes 3e, edited by John A. H. Wass, Wiebke Arlt, and Robert K. Semple, 1584–90. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780198870197.003.0490.
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Testosterone has been increasingly prescribed in recent years, particularly for middle-aged and older men with relatively non-specific symptoms that mimic androgen deficiency. There has thus been considerable interest in understanding the risk–benefit ratio of testosterone treatment in older men who may be particularly vulnerable to some of the adverse effects of exogenous testosterone. The exact risks and patient-important benefits are currently unknown due to lack of data from adequate randomized control trials, but some helpful inferences can be made based on available data. Erythrocytosis, the most commonly reported adverse effect of testosterone therapy, has been reported to be 3–4 times more likely in men treated with testosterone as compared to placebo; however, the form of therapy may play a role with the highest risk seen in men receiving intramuscular testosterone therapy as compared to transdermal formulations. Reassuringly, current data do not demonstrate that exogenous testosterone causes de novo or worsens mild to moderate lower urinary tract symptoms; insufficient data exist in men with severe lower urinary tract symptoms as such men have usually been excluded from testosterone trials. Testosterone has not been demonstrated to cause prostate cancer, but is not recommended presently in men who either have a history of prostate cancer or are otherwise at high risk. The effects of testosterone treatment on cardiovascular disease risk are unknown. The majority of presently available data do not suggest an increased risk, but in the absence of adequately powered randomized controlled trials, caution in men with pre-existing cardiovascular disease is recommended. Overall, further data are needed to better understand both the benefits and risks of exogenous testosterone in older men.
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Lewis, William M. "The Cast of Characters." In Wetlands Explained. Oxford University Press, 2001. http://dx.doi.org/10.1093/oso/9780195131833.003.0008.
Full textAbstract:
Through the inexorable workings of natural selection, wetlands have come to support a group of species that are especially well adapted to the physical and chemical peculiarities of saturated substrates and shallow water. These species give wetlands their distinctive biological signature and sustain the biotic functions of wetlands (Tiner 1998). The species that occupy wetlands show varying degrees of specialization. Some are so highly specialized that they can live or reproduce only in wetlands; these are called obligate wetland species. A second group contains organisms that are adapted for life in wetlands but are not restricted to wetlands; these are called facultative wetland species. Facultative wetland species show every conceivable shade of association with wetlands: they range from almost obligate to barely facultative. The distribution of obligate wetland species coincides closely with the distribution of wetlands. Thus, one could be tempted to rely heavily on obligate wetland species to find and map wetlands. Inference from obligate wetland species, however, must be tempered with caution. First, the absence of obligate wetland species does not necessarily mean absence of wetland, given that a wetland can be mostly or even entirely occupied by facultative wetland species. In addition, the degree to which a given species is a wetland obligate may not be known with absolute certainty. Where a genetic variant or an unusual set of physical conditions prevails, a species that seemed to be obligate in other situations may prove to be merely facultative and thus not diagnostic proof of the presence of wetland. The perils of absolute reliance on obligate wetland species have turned the attention of wetland mappers to the analysis of entire communities. Although community analysis can be done in a number of ways, the central idea is to score a community according to the proportionate representation of species that show a known facultative or obligate affinity with wetland conditions. The analysis of communities for the purpose of mapping and identifying wetlands has been focused almost entirely on vascular plants (grasses, forbs, shrubs, and trees). Two good reasons for this are the relative ease with which plant communities can be analyzed and the immobility of plants.
Conference papers on the topic "Cautious inference"
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Svatos, Martin. "Cautious Rule-Based Collective Inference." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/922.
Full textAbstract:
Collective inference is a popular approach for solving tasks as knowledge graph completion within the statistical relational learning field. There are many existing solutions for this task, however, each of them is subjected to some limitation, either by restriction to only some learning settings, lacking interpretability of the model or theoretical test error bounds. We propose an approach based on cautious inference process which uses first-order rules and provides PAC-style bounds.
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Oramas M., Jose, Luc De Raedt, and Tinne Tuytelaars. "Towards cautious collective inference for object verification." In 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2014. http://dx.doi.org/10.1109/wacv.2014.6836089.
Full textReports on the topic "Cautious inference"
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McDowell, Luke K., Kalyan M. Gupta, and David W. Aha. Cautious Inference in Collective Classification. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada479429.
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