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Literatura académica sobre el tema "Post-hoc"

Autor: Grafiati

Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Post-hoc"

Grouse, Lawrence. "Post hoc ergo propter hoc". Journal of Thoracic Disease 8, n.º 7 (julio de 2016): E511—E512. http://dx.doi.org/10.21037/jtd.2016.04.49.

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Costello, E. Jane. "Post Hoc, Ergo Propter Hoc". American Journal of Psychiatry 174, n.º 4 (abril de 2017): 305–6. http://dx.doi.org/10.1176/appi.ajp.2016.16111320.

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Hoyt, David B. "Post hoc ergo propter hoc". Journal of Trauma and Acute Care Surgery 74, n.º 1 (enero de 2013): 8–16. http://dx.doi.org/10.1097/ta.0b013e31827dc6d3.

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Kassirer, Jerome P. y Richard I. Kopelman. "Post Hoc, Ergo Propter Hoc". Hospital Practice 22, n.º 7 (15 de julio de 1987): 69–73. http://dx.doi.org/10.1080/21548331.1987.11703268.

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North, Richard B. "Post Hoc Ergo Propter Hoc?" Neuromodulation: Technology at the Neural Interface 14, n.º 5 (septiembre de 2011): 485. http://dx.doi.org/10.1111/j.1525-1403.2011.00401.x.

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Bullock, John D. "Post Hoc, Ergo Propter Hoc". Survey of Ophthalmology 45, n.º 4 (enero de 2001): 355–57. http://dx.doi.org/10.1016/s0039-6257(00)00193-4.

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Srinivas, Titte R., Bing Ho, Joseph Kang y Bruce Kaplan. "Post Hoc Analyses". Transplantation 99, n.º 1 (enero de 2015): 17–20. http://dx.doi.org/10.1097/tp.0000000000000581.

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Bababekov, Yanik J. y David C. Chang. "Post Hoc Power". Annals of Surgery 269, n.º 1 (enero de 2019): e11-e12. http://dx.doi.org/10.1097/sla.0000000000002914.

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Konjikušić, Snežana, Slađana Starčević y Saša Mašić. "Post Hoc Analysis of Serbian hotel ratings". Industrija 47, n.º 3 (2019): 7–22. http://dx.doi.org/10.5937/industrija47-22081.

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Curran-Everett, Douglas y Henry Milgrom. "Post-hoc data analysis". Current Opinion in Allergy and Clinical Immunology 13, n.º 3 (junio de 2013): 223–24. http://dx.doi.org/10.1097/aci.0b013e3283609831.

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Tesis sobre el tema "Post-hoc"

Jeyasothy, Adulam. "Génération d'explications post-hoc personnalisées". Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS027.

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La thèse se place dans le domaine de l'IA explicable (XAI, eXplainable AI). Nous nous concentrons sur les méthodes d'interprétabilité post-hoc qui visent à expliquer à un utilisateur la prédiction pour une donnée d'intérêt spécifique effectuée par un modèle de décision entraîné. Pour augmenter l'interprétabilité des explications, cette thèse étudie l'intégration de connaissances utilisateur dans ces méthodes, et vise ainsi à améliorer la compréhensibilité de l'explication en générant des explications personnalisées adaptées à chaque utilisateur. Pour cela, nous proposons un formalisme général qui intègre explicitement la connaissance via un nouveau critère dans les objectifs d'interprétabilité. Ce formalisme est ensuite décliné pour différents types connaissances et différents types d'explications, particulièrement les exemples contre-factuels, conduisant à la proposition de plusieurs algorithmes (KICE, Knowledge Integration in Counterfactual Explanation, rKICE pour sa variante incluant des connaissances exprimées par des règles et KISM, Knowledge Integration in Surrogate Models). La question de l'agrégation des contraintes de qualité classique et de compatibilité avec les connaissances est également étudiée et nous proposons d'utiliser l'intégrale de Gödel comme opérateur d'agrégation. Enfin nous discutons de la difficulté à générer une unique explication adaptée à tous types d'utilisateurs et de la notion de diversité dans les explications
This thesis is in the field of eXplainable AI (XAI). We focus on post-hoc interpretability methods that aim to explain to a user the prediction for a specific data made by a trained decision model. To increase the interpretability of explanations, this thesis studies the integration of user knowledge into these methods, and thus aims to improve the understandability of the explanation by generating personalized explanations tailored to each user. To this end, we propose a general formalism that explicitly integrates knowledge via a new criterion in the interpretability objectives. This formalism is then declined for different types of knowledge and different types of explanations, particularly counterfactual examples, leading to the proposal of several algorithms (KICE, Knowledge Integration in Counterfactual Explanation, rKICE for its variant including knowledge expressed by rules and KISM, Knowledge Integration in Surrogate Models). The issue of aggregating classical quality and knowledge compatibility constraints is also studied, and we propose to use Gödel's integral as an aggregation operator. Finally, we discuss the difficulty of generating a single explanation suitable for all types of users and the notion of diversity in explanations

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Sebyhed, Hugo y Emma Gunnarsson. "The Impotency of Post Hoc Power". Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-433274.

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In this thesis, we hope to dispel some confusion regarding the so-called post hoc power, i.e. power computed making the assumption that the estimated sample effect is equal to the population effect size. In previous research, it has been shown that post hoc power is a function of the p-value, making it redundant as a tool of analysis. We go further, arguing for it to never be reported, since it is a source of confusion and potentially harmful incentives. We also conduct a Monte Carlo simulation to illustrate our points of view. Previous research is confirmed by the results of this study.

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Galoob, Robert Paul. "Post hoc propter hoc| The impact of martyrdom on the development of Hasidut Ashkenaz". Thesis, Graduate Theological Union, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10646811.

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This dissertation explores the close literary, thematic and linguistic relationships between The Hebrew Chronicles of the First Crusade and the later pietistic text Sefer Hasidim. Despite a long-standing tendency to view the Jewish martyrdom of 1096 and the development of German pietism (Hasidut Ashkenaz) as unrelated. upon closer scrutiny, we find strong ties between the two texts. Sefer Hasidim, the most well-known pietistic text, contains dozens of martyrological stories and references that share similar language, themes and contexts as the crusade chronicles. Indeed, rather than standing alone, and unrelated to the first crusade literature, we find tales of martyrdom that closely resemble those in the first crusade narratives. Sefer Hasidim also contains numerous statements that indicate the primacy of martyrdom within the hierarchy of the pietistic belief system, while other martyrological references function as prooftext for the traditional pietistic themes distilled by Ivan Marcus and Haym Soloveitchik. The extent to which martyrological themes are integrated into the belief system articulated in Sefer Hasidim indicates that the martyrdom of the First Crusade should be viewed as formative to the development of Hasidut Ashkenaz. A close reading of Sefer Hasidim conclusively demonstrates this premise. Moreover, a similar analysis of the crusade chronicles reveals a wide range of martyrological tales described in quintessential pietistic terms; expressions of the will of God, the fear of God. and the pietistic preference for life in the hereafter, are found throughout the martyrological text.

When reading these two diverse texts side by side, we find substantive elements of a common world view spanning the period of the first crusade through the appearance of Sefer Hasidim. This allows us to understand each text through a new lens; the crusade chronicles now appear to be an early articulation of pietistic thought, while the later pietistic text now reads in part as a martyrological document of great significance.

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Durand, Guillermo. "Tests multiples et bornes post hoc pour des données hétérogènes". Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS289/document.

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Ce manuscrit présente mes contributions dans trois domaines des tests multiples où l'hétérogénéité des données peut être exploitée pour mieux détecter le signal tout en contrôlant les faux positifs : pondération des p-valeurs, tests discrets, et inférence post hoc. Premièrement, une nouvelle classe de procédures avec pondération données-dépendante, avec une structure de groupe et des estimateurs de la proportion de vraies nulles, est définie, et contrôle le False Discovery Rate (FDR) asymptotiquement. Cette procédure atteint aussi l'optimalité en puissance sous certaines conditions sur les estimateurs. Deuxièmement, de nouvelles procédures step-up et step-down, adaptées aux tests discrets sous indépendance, sont conçues pour contrôler le FDR pour une distribution arbitraire des marginales des p-valeurs sous l'hypothèse nulle. Finalement, de nouvelles familles de référence pour l'inférence post hoc, adaptées pour le cas où le signal est localisé, sont étudiées, et on calcule les bornes post hoc associées avec un algorithme simple
This manuscript presents my contributions in three areas of multiple testing where data heterogeneity can be exploited to better detect false null hypotheses or improve signal detection while controlling false positives: p-value weighting, discrete tests, and post hoc inference. First, a new class of data-driven weighting procedures, incorporating group structure and true null proportion estimators, is defined, and its False Discovery Rate (FDR) control is proven asymptotically. This procedure also achieves power optimality under some conditions on the proportion estimators. Secondly, new step-up and step-down procedures, tailored for discrete tests under independence, are designed to control the FDR for arbitrary p-value null marginals. Finally, new confidence bounds for post hoc inference (called post hoc bounds), tailored for the case where the signal is localized, are studied, and the associated optimal post hoc bounds are derived with a simple algorithm

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Laugel, Thibault. "Interprétabilité locale post-hoc des modèles de classification "boites noires"". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS215.

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Cette thèse porte sur le domaine du XAI (explicabilité de l'IA), et plus particulièrement sur le paradigme de l'interprétabilité locale post-hoc, c'est-à-dire la génération d'explications pour une prédiction unique d'un classificateur entraîné. En particulier, nous étudions un contexte totalement agnostique, c'est-à-dire que l'explication est générée sans utiliser aucune connaissance sur le modèle de classification (traité comme une boîte noire) ni les données utilisées pour l'entraîner. Dans cette thèse, nous identifions plusieurs problèmes qui peuvent survenir dans ce contexte et qui peuvent être préjudiciables à l'interprétabilité. Nous nous proposons d'étudier chacune de ces questions et proposons des critères et des approches nouvelles pour les détecter et les caractériser. Les trois questions sur lesquelles nous nous concentrons sont : le risque de générer des explications qui sont hors distribution ; le risque de générer des explications qui ne peuvent être associées à aucune instance d'entraînement ; et le risque de générer des explications qui ne sont pas assez locales. Ces risques sont étudiés à travers deux catégories spécifiques d'approches de l'interprétabilité : les explications contrefactuelles et les modèles de substitution locaux
This thesis focuses on the field of XAI (eXplainable AI), and more particularly local post-hoc interpretability paradigm, that is to say the generation of explanations for a single prediction of a trained classifier. In particular, we study a fully agnostic context, meaning that the explanation is generated without using any knowledge about the classifier (treated as a black-box) nor the data used to train it. In this thesis, we identify several issues that can arise in this context and that may be harmful for interpretability. We propose to study each of these issues and propose novel criteria and approaches to detect and characterize them. The three issues we focus on are: the risk of generating explanations that are out of distribution; the risk of generating explanations that cannot be associated to any ground-truth instance; and the risk of generating explanations that are not local enough. These risks are studied through two specific categories of interpretability approaches: counterfactual explanations, and local surrogate models

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Radulovic, Nedeljko. "Post-hoc Explainable AI for Black Box Models on Tabular Data". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT028.

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Les modèles d'intelligence artificielle (IA) actuels ont fait leurs preuves dans la résolution de diverses tâches, telles que la classification, la régression, le traitement du langage naturel (NLP) et le traitement d'images. Les ressources dont nous disposons aujourd'hui nous permettent d'entraîner des modèles d'IA très complexes pour résoudre différents problèmes dans presque tous les domaines : médecine, finance, justice, transport, prévisions, etc. Avec la popularité et l'utilisation généralisée des modèles d'IA, la nécessite d'assurer la confiance dans ces modèles s'est également accrue. Aussi complexes soient-ils aujourd'hui, ces modèles d'IA sont impossibles à interpréter et à comprendre par les humains. Dans cette thèse nous nous concentrons sur un domaine de recherche spécifique, à savoir l'intelligence artificielle explicable (xAI), qui vise à fournir des approches permettant d'interpréter les modèles d'IA complexes et d'expliquer leurs décisions. Nous présentons deux approches, STACI et BELLA, qui se concentrent sur les tâches de classification et de régression, respectivement, pour les données tabulaires. Les deux méthodes sont des approches post-hoc agnostiques au modèle déterministe, ce qui signifie qu'elles peuvent être appliquées à n'importe quel modèle boîte noire après sa création. De cette manière, l'interopérabilité présente une valeur ajoutée sans qu'il soit nécessaire de faire des compromis sur les performances du modèle de boîte noire. Nos méthodes fournissent des interprétations précises, simples et générales à la fois de l'ensemble du modèle boîte noire et de ses prédictions individuelles. Nous avons confirmé leur haute performance par des expériences approfondies et étude d'utilisateurs
Current state-of-the-art Artificial Intelligence (AI) models have been proven to be verysuccessful in solving various tasks, such as classification, regression, Natural Language Processing(NLP), and image processing. The resources that we have at our hands today allow us to trainvery complex AI models to solve different problems in almost any field: medicine, finance, justice,transportation, forecast, etc. With the popularity and widespread use of the AI models, the need toensure the trust in them also grew. Complex as they come today, these AI models are impossible to be interpreted and understood by humans. In this thesis, we focus on the specific area of research, namely Explainable Artificial Intelligence (xAI), that aims to provide the approaches to interpret the complex AI models and explain their decisions. We present two approaches STACI and BELLA which focus on classification and regression tasks, respectively, for tabular data. Both methods are deterministic model-agnostic post-hoc approaches, which means that they can be applied to any black-box model after its creation. In this way, interpretability presents an added value without the need to compromise on black-box model's performance. Our methods provide accurate, simple and general interpretations of both the whole black-box model and its individual predictions. We confirmed their high performance through extensive experiments and a user study

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Lowman, Lisa. "A post-hoc assessment of the Assiniboine-La Salle River Diversion project". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62785.pdf.

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Marchal, Cynthie. "Post-hoc prescience: retrospective reasoning and judgment among witnesses of interpersonal aggression". Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209818.

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When judging interpersonal aggression, witnesses are usually expected to rationally consider, based on the evidence they have, what another reasonable person could (or should) have thought, known and done. However their analysis may be affected by judgment biases and personal motivations. These evaluative and retrospective biases, as well as the ascription of blame, are the main interests of this research. More specifically, we investigated the consequences of witnesses being prone to the hindsight bias, which is a common bias that gives individuals the feeling that they would have been able to predict past events, what in fact, is not the case. This process may have important effects on the victim, who “should have known” that an aggression would happen to him/her. In this dissertation, we examine the moderators of this bias and the role of the communication context in which it develops. We hypothesized that the communication context might affect the perspective that is taken on the event of interpersonal aggression and the perceived distance towards it. Also, we expected that the hindsight bias and victim blame would be decreased when reducing the psychological distance towards the event (i.e. perceived temporal distance and perceived proximity with the victim’s fate). In a same vein, we expected that the aggressor would be more derogated in this condition. The first four studies were designed to investigate the role of communication goals about the aggression. Asking participants to describe how (vs. why) the aggression happened was expected to diminish the perceived distance. The following study (study 5) examined whether reporting the event in the passive voice (vs. active voice) would have a similar effect. The four last studies investigated how the time of presenting the event (before vs. after its antecedents) would influence the perception of distance towards the events and the judgments. We expected that knowing the outcome initially might reduce the perceived distance with the events. Results of the first five studies confirmed the main hypotheses: the communication context that focused on the “how” of the event or that presented it in the passive voice reduced the perceived distance and diminished the predictability of the aggression and victim derogation. It also increases the derogation of the aggressor. In addition, the latter studies revealed that learning about the outcome right away leads to reduced derogation of the perpetrator and increased derogation of the victim, even when reducing the perceived distance with the event. Overall, this research suggests that the communication context in which the hindsight bias emerges, as well as the perceived distance with the negative event, are important factors when examining the retrospective reasoning and judgments of witnesses.

Lorsque les témoins jugent une agression interpersonnelle, il est généralement attendu d’eux qu’ils considèrent rationnellement ce qu’une personne raisonnable aurait pu penser, savoir et faire dans pareille situation, et ce en se fondant uniquement sur les preuves qui leur sont fournies. Il n’en reste pas moins que leur analyse sera toutefois tronquée par des biais de jugement et des motivations personnelles. C’est pourquoi la détermination du blâme et l’influence des déformations rétrospectives et évaluatives sont au cœur de cette recherche. Ainsi, nous investiguons plus particulièrement le biais de rétrospection, à savoir l’erreur commune qui laisse à l’individu penser qu’il est en mesure de prévoir n’importe quel événement, alors qu’en réalité, il n’en est rien. Une telle erreur peut cependant avoir de graves conséquences pour la victime dès lors que les témoins sont amenés à croire qu’elle aurait « dû » prévoir ce qui allait survenir. Dans cette thèse, nous envisageons également les modérateurs de ce biais, dont le rôle du contexte communicationnel. Nous avons, dès lors, fait l’hypothèse que le contexte communicationnel pourrait affecter l’angle sous lequel les témoins considèrent l’événement et la distance perçue par rapport à celui-ci. Ce faisant, nous pensions que le biais de rétrospection et le blâme de la victime seraient réduits lorsque le contexte diminuait la distance perçue vis-à-vis de l’événement (en l’occurrence, la distance temporelle et la proximité perçue avec le sort de la victime). De même, il était attendu que l’agresseur soit davantage blâmé dans pareille condition. Les quatre premières études s’intéressaient donc au rôle des buts poursuivis lors de la communication à propos de l’agression, afin d’envisager en quoi décrire comment (vs. pourquoi) l’agression s’était produite aidait à réduire la distance perçue. Une cinquième étude nous a ensuite permis de considérer si la voix passive (versus active) avait aussi un effet similaire. Quant aux quatre dernières études, elles avaient pour objectif d’investiguer dans quelle mesure l’ordre de présentation des informations (connaître la fin avant, vs. après les antécédents) pouvait avoir également une incidence sur la prise de distance par rapport à l’événement et aux jugements. Plus précisément, nous faisions l’hypothèse que connaître l’événement en premier lieu (avant ses antécédents) facilitait la réduction de la distance perçue. Les résultats obtenus dans les cinq premières recherches semblaient confirmer nos hypothèses :Un contexte communicationnel qui réduisait la distance psychologique perçue par rapport à l’événement pouvait non seulement diminuer le biais de rétrospection et le blâme de la victime, mais augmenter aussi le blâme de l’agresseur. Toutefois, les dernières recherches ont semblé démontrer, a contrario, que connaître l’agression en premier lieu pouvait réduire le blâme de l’agresseur et augmenter celui de la victime, alors même que la distance perçue avec les événements était réduite. In fine, ce travail suggère donc que le contexte communicationnel, dans lequel le biais émerge, et la prise de distance face à l’événement négatif sont autant de pistes qu’il faudrait creuser à l’avenir pour mieux comprendre le raisonnement et les jugements rétrospectifs des témoins.
Doctorat en Sciences Psychologiques et de l'éducation
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Sobotková, Marika. "Neurofeedback aktivity amygdaly pomocí funkční magnetické rezonance". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378028.

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The aim of this diploma thesis is real-time fMRI neurofeedback. In this case, the activity of amygdala is monitored and controled by an emotional regulatory visual task. A procedure to process measured data online and to incorporate it into the stimulus protocol has been proposed. A pilot study was carried out. Offline analysis of measured data was performed, including evaluation of the results of the analysis. The data is processed in MATLAB using the functions of the SPM library.

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SEVESO, ANDREA. "Symbolic Reasoning for Contrastive Explanations". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404830.

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La necessità di spiegazioni sui sistemi di Machine Learning (ML) sta crescendo man mano che i nuovi modelli superano in performance i loro predecessori, diventando più complessi e meno comprensibili per gli utenti finali. Un passaggio essenziale nella ricerca in ambito eXplainable Artificial Intelligence (XAI) è la creazione di modelli interpretabili che mirano ad approssimare la funzione decisionale di un algoritmo black box. Sebbene negli ultimi anni siano stati proposti diversi metodi di XAI, non è stata prestata sufficiente attenzione alla spiegazione di come i modelli modificano il loro comportamento in contrasto con altre versioni (ad esempio, a causa di nuovi addestramenti dei modelli o modifica dei dati sottostanti). In questi casi, un sistema XAI dovrebbe spiegare perché il modello cambia le sue previsioni sui risultati passati. In diverse situazioni pratiche, i decisori umani si confrontano con più di un modello di apprendimento automatico. Di conseguenza, sta crescendo l'importanza di capire come funzionano due modelli di Machine Learning al di là delle loro performance predittive, per comprendere il loro comportamento, le loro differenze e la loro somiglianza. Ad oggi, i modelli interpretabili sono sintetizzati per spiegare i cosiddetti modelli black-box e le loro previsioni, e possono essere utili per rappresentare formalmente e misurare le differenze nel comportamento del modello ri-addestrato nel trattare dati nuovi e diversi. Catturare e comprendere tali differenze è fondamentale, poiché la necessità di fiducia è fondamentale in qualsiasi applicazione a supporto dei processi decisionali umani-IA. Questa è l'idea di ContrXT, un nuovo approccio che (i) traccia i criteri decisionali di un classificatore black box codificando i cambiamenti nella logica decisionale attraverso Binary Decision Diagrams. Quindi (ii) fornisce spiegazioni globali, agnostici dalla tipologia di modello, Model-Contrastive (M-contrast) in linguaggio naturale, stimando perché -e in quale misura- il modello ha modificato il suo comportamento nel tempo. Abbiamo implementato e valutato questo approccio su diversi modelli ML supervisionati addestrati su set di dati di benchmark e un'applicazione reale, dimostrando che è efficace nel rilevare classi notevolmente modificate e nello spiegare la loro variazione attraverso un user study. L'approccio è stato implementato ed è disponibile per la comunità sia come pacchetto Python che tramite API REST, fornendo contrastive explanations as a service.
The need for explanations of Machine Learning (ML) systems is growing as new models outperform their predecessors while becoming more complex and less comprehensible for their end-users. An essential step in eXplainable Artificial Intelligence (XAI) research is to create interpretable models that aim at approximating the decision function of a black box algorithm. Though several XAI methods have been proposed in recent years, not enough attention was paid to explaining how models change their behaviour in contrast with other versions (e.g., due to retraining or data shifts). In such cases, an XAI system should explain why the model changes its predictions concerning past outcomes. In several practical situations, human decision-makers deal with more than one machine learning model. Consequently, the importance of understanding how two machine learning models work beyond their prediction performances is growing, to understand their behavior, their differences, and their likeness. To date, interpretable models are synthesised for explaining black boxes and their predictions and can be beneficial for formally representing and measuring the differences in the retrained model's behaviour in dealing with new and different data. Capturing and understanding such differences is crucial, as the need for trust is key in any application to support human-Artificial Intelligence (AI) decision-making processes. This is the idea of ContrXT, a novel approach that (i) traces the decision criteria of a black box classifier by encoding the changes in the decision logic through Binary Decision Diagrams. Then (ii) it provides global, model-agnostic, Model-Contrastive (M-contrast) explanations in natural language, estimating why -and to what extent- the model has modified its behaviour over time. We implemented and evaluated this approach over several supervised ML models trained on benchmark datasets and a real-life application, showing it is effective in catching majorly changed classes and in explaining their variation through a user study. The approach has been implemented, and it is available to the community both as a python package and through REST API, providing contrastive explanations as a service.

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Libros sobre el tema "Post-hoc"

Post hoc. Bolinas, CA: Avenue B, 1990.

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Rea, Elsbeth. Post hoc survey, final report. Belfast: Northern Ireland Post Qualifying Education and Training Partnership, 2002.

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United States. Congress. House. Committee on Post Office and Civil Service. Ad Hoc Task Force on Presidential Pay Recommendations. Presidential pay recommendations: Hearings before the Ad Hoc Task Force on Presidnetial Pay Recommendations of the Committee on Post Office and Civil Service, House of Representatives, One Hundredth Congress, first session, February 2 and 3, 1987. Washington: U.S. G.P.O., 1987.

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Post-catastrophe crisis: Addressing the dramatic need and scant availability of mental health care in the Gulf Coast : hearing before the Ad Hoc Subcommittee on Disaster Recovery of the Committee on Homeland Security and Governmental Affairs, United States Senate, One Hundred Tenth Congress, first session, October 31, 2007. Washington: U.S. G.P.O., 2008.

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United States. Congress. Senate. Committee on Homeland Security and Governmental Affairs. Ad Hoc Subcommittee on Disaster Recovery. Post-catastrophe crisis: Addressing the dramatic need and scant availability of mental health care in the Gulf Coast : hearing before the Ad Hoc Subcommittee on Disaster Recovery of the Committee on Homeland Security and Governmental Affairs, United States Senate, One Hundred Tenth Congress, first session, October 31, 2007. Washington: U.S. G.P.O., 2008.

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Planning for post-catastrophe housing needs: Has FEMA developed an effective strategy for housing large numbers of citizens displaced by disasters? : hearing before the Ad Hoc Subcommittee on Disaster Recovery of the Committee on Homeland Security and Governmental Affairs, United States Senate, One Hundred Tenth Congress, second session, July 30, 2008. Washington: U.S. G.P.O., 2009.

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Mommsen, Jens Karl Friedrich. Wider das Post Hoc Ergo Propter Hoc. Creative Media Partners, LLC, 2018.

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Dane Mitchell: Post Hoc. Mousse Magazine and Publishing, 2019.

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Jeffery, Commission y Moloo Rahim. 11 Post-Award Applications. Oxford University Press, 2018. http://dx.doi.org/10.1093/law/9780198729037.003.0011.

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This chapter focuses on International Centre for Settlement of Investment Disputes (ICSID) and UNCITRAL tribunal practice on post-award applications. It begins with a discussion of certain post-award remedies under the ICSID Convention and ICSID Additional-Facility arbitral rules, including requests for supplementary decision, rectification/correction, interpretation and revision. It then considers annulment under the ICSID Convention, with emphasis on the conduct and procedure for annulment proceedings, the number of annulment applications and rates of success, and the constitution and composition of ad-hoc committees. It also examines the practice of stays of enforcement in annulment proceedings, and in particular the relatively long line of ICSID decisions rendered by ad-hoc committees on such applications. Finally, it outlines the post-award remedies available to parties under the UNCITRAL Arbitration Rules.

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Reimmann, Jacob Friedrich. Jacobi Friderici Reimmanni Ilias Post Homerum: Hoc Est, Incunabula Omnium Scientiarum Ex Homero Eruta Et Systematice Descripta. Arkose Press, 2015.

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Capítulos de libros sobre el tema "Post-hoc"

Pinto, Robert C. "Post Hoc, Ergo Propter Hoc". En Argument, Inference and Dialectic, 56–63. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-017-0783-1_6.

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Cummings, Louise. "Post Hoc, Ergo Propter Hoc". En Fallacies in Medicine and Health, 231–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-28513-5_7.

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Armstrong, Richard A. y Anthony C. Hilton. "Post Hoc Tests". En Statistical Analysis in Microbiology: Statnotes, 39–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch7.

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Kamath, Uday y John Liu. "Post-Hoc Interpretability and Explanations". En Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning, 167–216. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83356-5_5.

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Nichols, Lionel. "Rule of Law Reforms: Post Hoc Ergo Propter Hoc?" En The International Criminal Court and the End of Impunity in Kenya, 183–231. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10729-5_7.

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Wolfram, Dietmar. "Clustering for Post Hoc Information Retrieval". En Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_950-2.

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Wolfram, Dietmar. "Clustering for Post Hoc Information Retrieval". En Encyclopedia of Database Systems, 375–78. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_950.

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Holcomb, Zealure C. y Keith S. Cox. "Chi-Square and Post Hoc Tests". En Interpreting Basic Statistics, 204–6. Eighth edition. | New York, NY : Routledge, 2018.: Routledge, 2017. http://dx.doi.org/10.4324/9781315225647-64.

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Cox, Keith S. y Zealure C. Holcomb. "Chi-Square with Post Hoc Tests". En Interpreting Basic Statistics, 212–14. 9^a ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003096764-63.

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Wolfram, Dietmar. "Clustering for Post Hoc Information Retrieval". En Encyclopedia of Database Systems, 484–88. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_950.

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Actas de conferencias sobre el tema "Post-hoc"

Andersson, Ola, Elenor Cacciatore, Jonas Löwgren y Thomas Lundin. "Post-hoc worknotes". En the tenth ACM international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/641007.641147.

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Shatnawi, Sara, Esra'a Odat, Maram Thiabat y Aya A. Al-Badarneh. "Cancer in Jordan: Post-Hoc Comparative Analysis". En 2022 13th International Conference on Information and Communication Systems (ICICS). IEEE, 2022. http://dx.doi.org/10.1109/icics55353.2022.9811179.

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Abdelali, Ahmed, Nadir Durrani, Fahim Dalvi y Hassan Sajjad. "Post-hoc analysis of Arabic transformer models". En Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.blackboxnlp-1.8.

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Azzouz, Sara, Logan A. Walker, Alexandra Doerner, Kellie L. Geisel, Arianna K. Rodríguez Rivera, Ye Li, Douglas H. Roossien y Dawen Cai. "Optimized Neuron Tracing Using Post Hoc Reanalysis". En 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE, 2023. http://dx.doi.org/10.1109/isbi53787.2023.10230710.

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Markos, Vassilis y Loizos Michael. "Post-hoc Diversity-aware Curation of Rankings". En 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010839600003116.

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Asano, Kohei y Jinhee Chun. "Post-hoc Global Explanation using Hypersphere Sets". En 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010819100003116.

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Zhou, Tongyu, Haoyu Sheng y Iris Howley. "Assessing Post-hoc Explainability of the BKT Algorithm". En AIES '20: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375627.3375856.

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Agranovsky, Alexy, David Camp, Christoph Garth, E. Wes Bethel, Kenneth I. Joy y Hank Childs. "Improved post hoc flow analysis via Lagrangian representations". En 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2014. http://dx.doi.org/10.1109/ldav.2014.7013206.

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Zhang, Jun. "Surrogate Based Post-HOC Calibration for Distributional Shift". En ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096090.

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Dua, Radhika, Seongjun Yang, Yixuan Li y Edward Choi. "Task Agnostic and Post-hoc Unseen Distribution Detection". En 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. http://dx.doi.org/10.1109/wacv56688.2023.00140.

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Informes sobre el tema "Post-hoc"

Anderson, Dave M. y Donna J. Hostick. Post Hoc Evaluation of Long-Term Goals for Energy Savings in the Buildings Sector: Lessons from Hindsight. Office of Scientific and Technical Information (OSTI), abril de 2003. http://dx.doi.org/10.2172/15003619.

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Moreland, Kenneth, Andrew Bauer, Berk Geveci, Patrick O'Leary y Brad Whitlock. Leveraging production visualization tools in situ. Engineer Research and Development Center (U.S.), julio de 2022. http://dx.doi.org/10.21079/11681/44844.

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The visualization community has invested decades of research and development into producing large-scale production visualization tools. Although in situ is a paradigm shift for large-scale visualization, much of the same algorithms and operations apply regardless of whether the visualization is run post hoc or in situ. Thus, there is a great benefit to taking the large-scale code originally designed for post hoc use and leveraging it for use in situ. This chapter describes two in situ libraries, Libsim and Catalyst, that are based on mature visualization tools, VisIt and ParaView, respectively. Because they are based on fully-featured visualization packages, they each provide a wealth of features. For each of these systems we outline how the simulation and visualization software are coupled, what the runtime behavior and communication between these components are, and how the underlying implementation works. We also provide use cases demonstrating the systems in action. Both of these in situ libraries, as well as the underlying products they are based on, are made freely available as open-source products. The overviews in this chapter provide a toehold to the practical application of in situ visualization.

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Zhang, Yunfeng y Anthony J. Hornof. Using the Mean Shift Algorithm to Make Post Hoc Improvements to the Accuracy of Eye Tracking Data Based on Probable Fixation Locations. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2010. http://dx.doi.org/10.21236/ada528607.

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Storm, Servaas. The Art of Paradigm Maintenance: How the ‘Science of Monetary Policy’ tries to deal with the inflation of 2021-2023. Institute for New Economic Thinking Working Paper Series, octubre de 2023. http://dx.doi.org/10.36687/inetwp214.

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The macroeconomic models used by major institutions including the Federal Reserve and the International Monetary Fund (IMF) failed to predict the inflation surge during 2021-2023. The output gap, the unemployment gap, the New Keynesian Phillips curve and inflation expectations did not give timely and relevant signals. The re-emergence of inflation thus threw the ‘science of monetary policy’ off the rails. Faced with the choice between changing their paradigm and proving that there is no need to do so, the ‘scientists of monetary policy’ got busy on the proof. As a result, a number of ad-hoc epicycles have been added to the New Keynesian analytical core—with the help of which one can claim to be able to explain the sudden acceleration of inflation post-factum. This paper critically reviews the theoretical and empirical merits of three recent tweaks to the New Keynesian core: using the vacancy ratio as the appropriate measure of real economic activity; hammering on the considerable risk of an imminent wage-price spiral; and the resurrection of the non-linear Phillips curve. The paper concludes by drawing out sobering lessons concerning the art of paradigm maintenance as practiced by the ‘scientists of monetary policy’

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