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Inductive Reasoning Games as Influenza Vaccination Models: Mean Field Analysis  [PDF]
Romulus Breban,Raffaele Vardavas,Sally Blower
Quantitative Biology , 2006, DOI: 10.1103/PhysRevE.76.031127
Abstract: We define and analyze an inductive reasoning game of voluntary yearly vaccination in order to establish whether or not a population of individuals acting in their own self-interest would be able to prevent influenza epidemics. We find that epidemics are rarely prevented. We also find that severe epidemics may occur without the introduction of pandemic strains. We further address the situation where market incentives are introduced to help ameliorating epidemics. Surprisingly, we find that vaccinating families exacerbates epidemics. However, a public health program requesting prepayment of vaccinations may significantly ameliorate influenza epidemics.
A frequentist framework of inductive reasoning  [PDF]
David R. Bickel
Mathematics , 2006, DOI: 10.1007/s13171-012-0020-x
Abstract: Reacting against the limitation of statistics to decision procedures, R. A. Fisher proposed for inductive reasoning the use of the fiducial distribution, a parameter-space distribution of epistemological probability transferred directly from limiting relative frequencies rather than computed according to the Bayes update rule. The proposal is developed as follows using the confidence measure of a scalar parameter of interest. (With the restriction to one-dimensional parameter space, a confidence measure is essentially a fiducial probability distribution free of complications involving ancillary statistics.) A betting game establishes a sense in which confidence measures are the only reliable inferential probability distributions. The equality between the probabilities encoded in a confidence measure and the coverage rates of the corresponding confidence intervals ensures that the measure's rule for assigning confidence levels to hypotheses is uniquely minimax in the game. Although a confidence measure can be computed without any prior distribution, previous knowledge can be incorporated into confidence-based reasoning. To adjust a p-value or confidence interval for prior information, the confidence measure from the observed data can be combined with one or more independent confidence measures representing previous agent opinion. (The former confidence measure may correspond to a posterior distribution with frequentist matching of coverage probabilities.) The representation of subjective knowledge in terms of confidence measures rather than prior probability distributions preserves approximate frequentist validity.
Dealing with uncertainty in fuzzy inductive reasoning methodology  [PDF]
Francisco Mugica,Angela Nebot,Pilar Gomez
Computer Science , 2012,
Abstract: The aim of this research is to develop a reasoning under uncertainty strategy in the context of the Fuzzy Inductive Reasoning (FIR) methodology. FIR emerged from the General Systems Problem Solving developed by G. Klir. It is a data driven methodology based on systems behavior rather than on structural knowledge. It is a very useful tool for both the modeling and the prediction of those systems for which no previous structural knowledge is available. FIR reasoning is based on pattern rules synthesized from the available data. The size of the pattern rule base can be very large making the prediction process quite difficult. In order to reduce the size of the pattern rule base, it is possible to automatically extract classical Sugeno fuzzy rules starting from the set of pattern rules. The Sugeno rule base preserves pattern rules knowledge as much as possible. In this process some information is lost but robustness is considerably increased. In the forecasting process either the pattern rule base or the Sugeno fuzzy rule base can be used. The first option is desirable when the computational resources make it possible to deal with the overall pattern rule base or when the extracted fuzzy rules are not accurate enough due to uncertainty associated to the original data. In the second option, the prediction process is done by means of the classical Sugeno inference system. If the amount of uncertainty associated to the data is small, the predictions obtained using the Sugeno fuzzy rule base will be very accurate. In this paper a mixed pattern/fuzzy rules strategy is proposed to deal with uncertainty in such a way that the best of both perspectives is used. Areas in the data space with a higher level of uncertainty are identified by means of the so-called error models. The prediction process in these areas makes use of a mixed pattern/fuzzy rules scheme, whereas areas identified with a lower level of uncertainty only use the Sugeno fuzzy rule base. The proposed strategy is applied to a real biomedical system, i.e., the central nervous system control of the cardiovascular system.
The role of the DLPFC in inductive reasoning of MCI patients and normal agings: An fMRI study
YanHui Yang,PeiPeng Liang,ShengFu Lu,KunCheng Li,Ning Zhong
Science China Life Sciences , 2009, DOI: 10.1007/s11427-009-0089-1
Abstract: Previous studies of young people have revealed that the left dorsolateral prefrontal cortex (DLPFC) plays an important role in inductive reasoning. An fMRI experiment was performed in this study to examine whether the left DLPFC was involved in inductive reasoning of MCI patients and normal agings, and whether the activation pattern of this region was different between MCI patients and normal agings. The fMRI results indicated that MCI patients had no difference from normal agings in behavior performance (reaction time and accuracy) and the activation pattern of DLPFC. However, the BOLD response of the DLPFC region for MCI patients was weaker than that for normal agings, and the functional connectivity between the bilateral DLPFC regions for MCI patients was significantly higher than for normal agings. Taken together, these results indicated that DLPFC plays an important role in inductive reasoning of agings, and the functional abnormity of DLPFC may be an earlier marker of MCI before structural alterations.
Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence  [PDF]
Marcus Hutter
Mathematics , 2011,
Abstract: This article is a brief personal account of the past, present, and future of algorithmic randomness, emphasizing its role in inductive inference and artificial intelligence. It is written for a general audience interested in science and philosophy. Intuitively, randomness is a lack of order or predictability. If randomness is the opposite of determinism, then algorithmic randomness is the opposite of computability. Besides many other things, these concepts have been used to quantify Ockham's razor, solve the induction problem, and define intelligence.
Determinants of vaccination coverage in rural Nigeria
Olumuyiwa O Odusanya, Ewan F Alufohai, Francois P Meurice, Vincent I Ahonkhai
BMC Public Health , 2008, DOI: 10.1186/1471-2458-8-381
Abstract: A cross-sectional survey was conducted in September 2006, which included the use of interviewer-administered questionnaire to assess knowledge of mothers of children aged 12–23 months and vaccination coverage. Survey participants were selected following the World Health Organization's (WHO) immunization coverage cluster survey design. Vaccination coverage was assessed by vaccination card and maternal history. A child was said to be fully immunized if he or she had received all of the following vaccines: a dose of Bacille Calmette Guerin (BCG), three doses of oral polio (OPV), three doses of diphtheria, pertussis and tetanus (DPT), three doses of hepatitis B (HB) and one dose of measles by the time he or she was enrolled in the survey, i.e. between the ages of 12–23 months. Knowledge of the mothers was graded as satisfactory if mothers had at least a score of 3 out of a maximum of 5 points. Logistic regression was performed to identify determinants of full immunization status.Three hundred and thirty-nine mothers and 339 children (each mother had one eligible child) were included in the survey. Most of the mothers (99.1%) had very positive attitudes to immunization and > 55% were generally knowledgeable about symptoms of vaccine preventable diseases except for difficulty in breathing (as symptom of diphtheria). Two hundred and ninety-five mothers (87.0%) had a satisfactory level of knowledge. Vaccination coverage against all the seven childhood vaccine preventable diseases was 61.9% although it was significantly higher (p = 0.002) amongst those who had a vaccination card (131/188, 69.7%) than in those assessed by maternal history (79/151, 52.3%). Multiple logistic regression showed that mothers' knowledge of immunization (p = 0.006) and vaccination at a privately funded health facility (p < 0.001) were significantly correlated with the rate of full immunization.Eight years after initiation of this privately financed vaccination project (private-public partnership), v
The Link between Seasonal Influenza and NCDs: Strategies for Improving Vaccination Coverage  [PDF]
Abraham Palache, Julia Tainijoki-Seyer, Téa Collins
Health (Health) , 2014, DOI: 10.4236/health.2014.619311
Abstract: Seasonal influenza is a major public health problem globally, causing significant morbidity and mortality, especially in high-risk groups. Children and adults with underlying chronic non-communicable diseases (NCDs) are especially vulnerable to complications, hospitalizations and even death from the infection. However, the link between NCDs and influenza is frequently underestimated. Vaccination against influenza is the single most effective way to reduce this vulnerability in people living with NCDs. Irrespective vaccination rates in this group fall short of the WHO recommended target of 75%. This paper explores the relationship between seasonal influenza and NCDs and proposes strategies for increasing vaccination coverage among the target groups.
Neural Mechanism of Figural Inductive Reasoning: An fMRI Study
图形型归纳推理的神经机制:一项fMRI研究

MEI Yang,LIANG Pei-Peng,LU Sheng-Fu,ZHONG Ning,LI Kun-Cheng,YANG Yan-Hui,
梅杨
,梁佩鹏,吕胜富,杨延辉,钟宁,李坤成

心理学报 , 2010,
Abstract: The neural mechanism of human inductive reasoning is still unclear. Compared with the sentential, numerical task, the figural inductive reasoning task has its advantage. Therefore, a figural inductive reasoning task was designed in an fMRI experiment to examine the neural substrates of human inductive reasoning. The present study is exploratory one and we have no prior hypothesis. The figural inductive reasoning task used was composed of simple geometric figures described by shape and stripe orientation, an...
Influenza vaccination coverage: findings from immunization information systems
Laura A Zimmerman, Diana L Bartlett, Kyle S Enger, Kimiko Gosney, Warren G Williams
BMC Pediatrics , 2007, DOI: 10.1186/1471-2431-7-28
Abstract: Well-functioning sentinel project immunization information systems (IIS) in Arizona (AIIS) and Michigan (MIIS) were used to calculate vaccination coverage among children aged 6–23 months during the 2004–05 influenza season. We calculated 2 measures of vaccination coverage: a) receipt of 1 or more doses of influenza vaccine September 2004-March 2005 and b) receipt of 2 or more doses (ie, fully vaccinated). We compared the dose administration distribution among children needing 1 and 2 doses and by provider type. Coverage by age and timeliness of vaccine doses entered into the IIS were also analyzed.Influenza vaccination coverage levels among children were 30% and 27% in AIIS and MIIS, respectively, for receipt of 1 or more doses; 13% and 11% of children, respectively, were fully vaccinated. Peaks in dose administration among children needing 1 and 2 doses were similar. There were differences in vaccine administration between public and private providers. Coverage was higher among younger children and over 75% of all influenza vaccine doses were entered into the IIS within 30 days after receipt of vaccine.Though almost 1/3 of children received 1 or more doses of vaccine in 2 IIS sentinel projects during the first season of the new recommendation, emphasis needs to be placed on increasing the proportion of children fully vaccinated. IIS data can be used for timely monitoring of vaccination coverage assessments.Influenza causes significant morbidity among children. In the United States, rates of influenza infection are highest among children, and those aged 6–23 months are at substantially increased risk for influenza-related hospitalizations [1]. The increased rates of hospitalizations are comparable with rates for other groups considered to be at high risk for influenza-related complications [2].Beginning in 2002, the Advisory Committee on Immunization Practices (ACIP) encouraged the vaccination of all children aged 6–23 months with influenza vaccine. Subsequently, be
Ozone prediction based on meteorological variables: a fuzzy inductive reasoning approach  [PDF]
A. Nebot,V. Mugica,A. Escobet
Atmospheric Chemistry and Physics Discussions , 2008,
Abstract: MILAGRO project was conducted in Mexico City during March 2006 with the main objective of study the local and global impact of pollution generated by megacities. The research presented in this paper is framed in MILAGRO project and is focused on the study and development of modeling methodologies that allow the forecasting of daily ozone concentrations. The present work aims to develop Fuzzy Inductive Reasoning (FIR) models using the Visual-FIR platform. FIR offers a model-based approach to modeling and predicting either univariate or multivariate time series. Visual-FIR offers an easy-friendly environment to perform this task. In this research, long term prediction of maximum ozone concentration in the downtown of Mexico City Metropolitan Area is performed. The data were registered every hour and include missing values. Two modeling perspectives are analyzed, i.e. monthly and seasonal models. The results show that the developed models are able to predict the diurnal variation of ozone, including its maximum daily value in an accurate manner.
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