Search results for 'Parameter estimation' (try it on Scholar)

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  1.  23
    Richard Scheines, Estimating Latent Causal Influences: Tetrad III Variable Selection and Bayesian Parameter Estimation.
    The statistical evidence for the detrimental effect of exposure to low levels of lead on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics help make the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD III for finding causes, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead (...)
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  2.  32
    Jerry Batzel & Mostafa Bachar (2010). Modeling the Cardiovascular-Respiratory Control System: Data, Model Analysis, and Parameter Estimation. Acta Biotheoretica 58 (4):369-380.
    Several key areas in modeling the cardiovascular and respiratory control systems are reviewed and examples are given which reflect the research state of the art in these areas. Attention is given to the interrelated issues of data collection, experimental design, and model application including model development and analysis. Examples are given of current clinical problems which can be examined via modeling, and important issues related to model adaptation to the clinical setting.
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  3.  10
    M. I. Charles E. Woodson (1969). Parameter Estimation Vs. Hypothesis Testing. Philosophy of Science 36 (2):203-204.
  4.  1
    Ernst PÖppel (1975). Parameter Estimation or Hypothesis Testing in the Statistical Analysis of Biological Rhythms? Bulletin of the Psychonomic Society 6 (5):511-512.
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  5. Tiffany S. Jastrzembski & Neil Charness (2007). The Model Human Processor and the Older Adult: Parameter Estimation and Validation Within a Mobile Phone Task. Journal of Experimental Psychology: Applied 13 (4):224-248.
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  6. Yuan Shi & Xing Zhong (2008). Hierarchical Differential Evolution for Parameter Estimation in Chemical Kinetics. In Tu-Bao Ho & Zhi-Hua Zhou (eds.), Pricai 2008: Trends in Artificial Intelligence. Springer 870--879.
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  7.  10
    Jean-franÇois Laslier (1989). Estimation of a Bernouilli Parameter: A Normative Approach to Replace the Bayesian One. Theory and Decision 26 (3):253.
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  8.  58
    Rolf Niedermeier (2006). Invitation to Fixed-Parameter Algorithms. Oxford University Press.
    A fixed-parameter is an algorithm that provides an optimal solution to a combinatorial problem. This research-level text is an application-oriented introduction to the growing and highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. The book is divided into three parts: a broad introduction that provides the general philosophy and motivation; followed by coverage of algorithmic methods developed over the years in fixed-parameter algorithmics forming the core of the book; and a (...)
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  9.  7
    Johan Grasman, Willem B. E. Van Deventer & Vincent van Laar (2012). Estimation of Parameters in a Bertalanffy Type of Temperature Dependent Growth Model Using Data on Juvenile Stone Loach (Barbatula Barbatula). Acta Biotheoretica 60 (4):393-405.
    Parameters of a Bertalanffy type of temperature dependent growth model are fitted using data from a population of stone loach ( Barbatula barbatula ). Over two periods respectively in 1990 and 2010 length data of this population has been collected at a lowland stream in the central part of the Netherlands. The estimation of the maximum length of a fully grown individual is given special attention because it is in fact found as the result of an extrapolation over a (...)
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  10.  6
    Mark Johnson & Stefan Riezler (2002). Statistical Models of Syntax Learning and Use. Cognitive Science 26 (3):239-253.
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  11.  13
    Christoph Graf, Rudolf Vetschera & Yingchao Zhang (2013). Parameters of Social Preference Functions: Measurement and External Validity. Theory and Decision 74 (3):357-382.
  12.  3
    Dan Klein & Christopher D. Manning, Conditional Structure Versus Conditional Estimation in NLP Models.
    This paper separates conditional parameter estima- tion, which consistently raises test set accuracy on statistical NLP tasks, from conditional model struc- tures, such as the conditional Markov model used for maximum-entropy tagging, which tend to lower accuracy. Error analysis on part-of-speech tagging shows that the actual tagging errors made by the conditionally structured model derive not only from label bias, but also from other ways in which the independence assumptions of the conditional model structure are unsuited to linguistic sequences. (...)
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  13.  29
    Stanley A. Mulaik (2001). The Curve-Fitting Problem: An Objectivist View. Philosophy of Science 68 (2):218-241.
    Model simplicity in curve fitting is the fewness of parameters estimated. I use a vector model of least squares estimation to show that degrees of freedom, the difference between the number of observed parameters fit by the model and the number of explanatory parameters estimated, are the number of potential dimensions in which data are free to differ from a model and indicate the disconfirmability of the model. Though often thought to control for parameter estimation, the AIC (...)
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  14.  10
    Mélanie Zetlaoui, Nicolas Picard & Avner Bar-hen (2008). Asymptotic Distribution of Density-Dependent Stage-Grouped Population Dynamics Models. Acta Biotheoretica 56 (1-2):137-155.
    Matrix models are widely used in biology to predict the temporal evolution of stage-structured populations. One issue related to matrix models that is often disregarded is the sampling variability. As the sample used to estimate the vital rates of the models are of finite size, a sampling error is attached to parameter estimation, which has in turn repercussions on all the predictions of the model. In this study, we address the question of building confidence bounds around the predictions (...)
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  15.  39
    Romeijn, J.-W., Statistics as Inductive Inference.
    This chapter1 concerns the relation between statistics and inductive logic. I start by describing induction in formal terms, and I introduce a general notion of probabilistic inductive inference. This provides a setting in which statistical procedures and inductive logics can be cap- tured. Speciacally, I discuss three statistical procedures (hypotheses testing, parameter estimation, and Bayesian statistics) and I show to what extend they can be captured by certain inductive logics. I end with some suggestions on how inductive.
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  16.  32
    Richard Scheines, The Tetrad Project: Constraint Based Aids to Causal Model Specification.
    The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model’s parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model’s specification. We begin by drawing the analogy between parameter estimation and model specification search. We then describe how the specification of a structural (...)
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  17.  18
    Otis Dudley Duncan (1986). Probability, Disposition, and the Inconsistency of Attitudes and Behavior. Synthese 68 (1):65 - 98.
    Inconsistency of attitudes and behavior is due to the probabilistic connection between responses or actions and the (not directly observable) dispositions on which they depend. Latent variable models provide criteria for recognizing when attitude and behavior depend on the same disposition. Statistical tests of such models and techniques of parameter estimation are described. The viewpoint proposed here and illustrated with empirical examples contrasts with the prevalent reliance on correlational models and methods.
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  18.  17
    Peter Spirtes, Clark Glymour & Richard Scheines, Automated Search for Causal Relations - Theory and Practice.
    nature of modern data collection and storage techniques, and the increases in the speed and storage capacities of computers. Statistics books from 30 years ago often presented examples with fewer than 10 variables, in domains where some background knowledge was plausible. In contrast, in new domains, such as climate research where satellite data now provide daily quantities of data unthinkable a few decades ago, fMRI brain imaging, and microarray measurements of gene expression, the number of variables can range into the (...)
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  19.  10
    Joseph F. Hanna (1978). On Transmitted Information as a Measure of Explanatory Power. Philosophy of Science 45 (4):531-562.
    This paper contrasts two information-theoretic approaches to statistical explanation: namely, (1) an analysis, which originated in my earlier research on problems of testing stochastic models of learning, based on an entropy-like measure of expected transmitted-information (and here referred to as the Expected-Information Model), and (2) the analysis, which was proposed by James Greeno (and which is closely related to Wesley Salmon's Statistical Relevance Model), based on the information-transmitted-by-a-system. The substantial differences between these analyses can be traced to the following basic (...)
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  20.  13
    John R. Vokey (1998). Statistics Without Probability: Significance Testing as Typicality and Exchangeability in Data Analysis. Behavioral and Brain Sciences 21 (2):225-226.
    Statistical significance is almost universally equated with the attribution to some population of nonchance influences as the source of structure in the data. But statistical significance can be divorced from both parameter estimation and probability as, instead, a statement about the atypicality or lack of exchangeability over some distinction of the data relative to some set. From this perspective, the criticisms of significance tests evaporate.
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  21. David G. Luenberger (2013). Investment Science. Oxford University Press Usa.
    Investment Science, Second Edition, provides thorough and highly accessible mathematical coverage of the fundamental topics of intermediate investments, including fixed-income securities, capital asset pricing theory, derivatives, and innovations in optimal portfolio growth and valuation of multi-period risky investments. Eminent scholar and teacher David G. Luenberger, known for his ability to make complex ideas simple, presents essential ideas of investments and their applications, offering students the most comprehensive treatment of the subject available. New to this edition Three new chapters: Risk Management, (...)
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  22.  86
    Iñaki San Pedro (2014). Measurement Independence, Parameter Independence and Non-Locality. European Journal for Philosophy of Science 4 (3):369-374.
    In a recent paper in this Journal San Pedro I formulated a conjecture relating Measurement Independence and Parameter Independence, in the context of common cause explanations of EPR correlations. My conjecture suggested that a violation of Measurement Independence would entail a violation of Parameter Independence as well. Leszek Wroński has shown that conjecture to be false. In this note, I review Wroński’s arguments and agree with him on the fate of the conjecture. I argue that what is interesting (...)
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  23. Eran Tal (2012). The Epistemology of Measurement: A Model-Based Account. Dissertation, University of Toronto
    This work develops an epistemology of measurement, that is, an account of the conditions under which measurement and standardization methods produce knowledge as well as the nature, scope, and limits of this knowledge. I focus on three questions: (i) how is it possible to tell whether an instrument measures the quantity it is intended to? (ii) what do claims to measurement accuracy amount to, and how might such claims be justified? (iii) when is disagreement among instruments a sign of error, (...)
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  24. Toby Ord, Rafaela Hillerbrand & Anders Sandberg, Probing the Improbable: Methodological Challenges for Risks with Low Probabilities and High Stakes.
    Some risks have extremely high stakes. For example, a worldwide pandemic or asteroid impact could potentially kill more than a billion people. Comfortingly, scientific calculations often put very low probabilities on the occurrence of such catastrophes. In this paper, we argue that there are important new methodological problems which arise when assessing global catastrophic risks and we focus on a problem regarding probability estimation. When an expert provides a calculation of the probability of an outcome, they are really providing (...)
     
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  25.  35
    Akop P. Nazaretyan (2005). Western and Russian Traditions of Big History: A Philosophical Insight. [REVIEW] Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 36 (1):63 - 80.
    Big History - an integral conception of the past since the Big Bang until today - is a novel subject of cross-disciplinary interest. The concept was construed in the 1980-1990s simultaneously in different countries, after relevant premises had matured in the sciences and humanities. Various versions and traditions of Big History are considered in the article. Particularly, most of the Western authors emphasize the idea of equilibrium, and thus reduce cosmic, biological, and social evolution to the mass-energy processes; the informational (...)
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  26.  28
    David Danks & Clark Glymour, Linearity Properties of Bayes Nets with Binary Variables.
    It is “well known” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables” sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by (...)
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  27.  32
    Clark Glymour, Linearity Properties of Bayes Nets with Binary Variables.
    It is “well known” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables” sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by (...)
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  28.  1
    Stanley J. Rule, Dwight W. Curtis & Robert P. Markley (1970). Input and Output Transformations From Magnitude Estimation. Journal of Experimental Psychology 86 (3):343.
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  29.  52
    Woosuk Park (2012). Abduction and Estimation in Animals. Foundations of Science 17 (4):321-337.
    One of the most pressing issues in understanding abduction is whether it is an instinct or an inference. For many commentators find it paradoxical that new ideas are products of an instinct and products of an inference at the same time. Fortunately, Lorenzo Magnani’s recent discussion of animal abduction sheds light on both instinctual and inferential character of Peircean abduction. But, exactly for what reasons are Peirce and Magnani so convinced that animal abduction can provide us with a novel perspective? (...)
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  30.  7
    Andrew Wuensche (1999). Classifying Cellular Automata Automatically: Finding Gliders, Filtering, and Relating Space-Time Patterns, Attractor Basins, and theZ Parameter. Complexity 4 (3):47-66.
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  31.  1
    Panna Lal Pradham & Paul J. Hoffman (1963). Effect of Spacing and Range of Stimuli on Magnitude Estimation Judgements. Journal of Experimental Psychology 66 (6):533.
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  32.  8
    A. Cordón‐Franco, A. Fernández‐Margarit & F. F. Lara‐Martín (2011). A Note on Parameter Free Π1‐Induction and Restricted Exponentiation. Mathematical Logic Quarterly 57 (5):444-455.
    We characterize the sets of all Π2 and all equation image theorems of IΠ−1 in terms of restricted exponentiation, and use these characterizations to prove that both sets are not deductively equivalent. We also discuss how these results generalize to n > 0. As an application, we prove that a conservation theorem of Beklemishev stating that IΠ−n + 1 is conservative over IΣ−n with respect to equation image sentences cannot be extended to Πn + 2 sentences. © 2011 WILEY-VCH Verlag (...)
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  33.  2
    Mark A. Pitt, Jay I. Myung, Maximiliano Montenegro & James Pooley (2008). Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example. Cognitive Science 32 (8):1285-1303.
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  34.  9
    Chi‐Ming Chang, Wen‐Chou Lin, Hsu‐Sung Kuo, Ming‐Fang Yen & Tony Hsiu‐Hsi Chen (2007). Estimation and Prediction System for Multi‐State Disease Process: Application to Analysis of Organized Screening Regime. Journal of Evaluation in Clinical Practice 13 (6):867-881.
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  35.  3
    William Bevan & Edward D. Turner (1964). Assimilation and Contrast in the Estimation of Number. Journal of Experimental Psychology 67 (5):458.
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  36.  4
    R. Rakkiyappan, A. Chandrasekar, S. Laksmanan & Ju H. Park (2013). State Estimation of Memristor‐Based Recurrent Neural Networks with Time‐Varying Delays Based on Passivity Theory. Complexity 19 (4):32-43.
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  37.  8
    Nilly Adam, Angelina D. Castro & Donald L. Clark (1974). Production, Estimation, and Reproduction of Time Intervals During Inhalation of a General Anesthetic in Man. Journal of Experimental Psychology 102 (4):609.
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  38.  1
    Ian Begg (1974). Estimation of Word Frequency in Continuous and Discrete Tasks. Journal of Experimental Psychology 102 (6):1046.
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  39.  5
    Eric D. Curton & Daniel S. Lordahl (1974). Effects of Attentional Focus and Arousal on Time Estimation. Journal of Experimental Psychology 103 (5):861.
  40.  3
    Emir H. Shuford (1961). Percentage Estimation of Proportion as a Function of Element Type, Exposure Time, and Task. Journal of Experimental Psychology 61 (5):430.
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  41.  3
    Robert J. Filer & Donald W. Meals (1949). The Effect of Motivating Conditions on the Estimation of Time. Journal of Experimental Psychology 39 (3):327.
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  42.  4
    John M. Lockhart (1967). Ambient Temperature and Time Estimation. Journal of Experimental Psychology 73 (2):286.
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  43.  2
    Robert D. Meade (1963). Effect of Motivation and Progress on the Estimation of Longer Time Intervals. Journal of Experimental Psychology 65 (6):564.
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  44.  1
    William W. Lambert, Richard L. Solomon & Peter D. Watson (1949). Reinforcement and Extinction as Factors in Size Estimation. Journal of Experimental Psychology 39 (5):637.
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  45.  5
    V. G. Schaefer & A. R. Gilliland (1938). The Relation of Time Estimation to Certain Physiological Changes. Journal of Experimental Psychology 23 (5):545.
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  46.  5
    Kurt J. Teller, Richard Dieter & Milton D. Suboski (1972). Time Estimation and the Interstimulus Interval Function in Classical Conditioning. Journal of Experimental Psychology 95 (2):445.
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  47.  3
    Ruthanne K. S. Dewolfe & Carl P. Duncan (1959). Time Estimation as a Function of Level of Behavior of Successive Tasks. Journal of Experimental Psychology 58 (2):153.
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  48.  1
    Darryl A. Yoblick & Gavriel Salvendy (1970). Influence of Frequency on the Estimation of Time for Auditory, Visual, and Tactile Modalities: The Kappa Effect. Journal of Experimental Psychology 86 (2):157.
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  49.  2
    Arthur B. Fitt (1917). The Estimation of Distances by Sight and Passive Touch: Some Investigations Into the Evolution of the Sense of Touch. Journal of Experimental Psychology 2 (4):264-288.
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  50.  2
    F. Nowell Jones & Morris J. Woskow (1966). Some Effects of Context on the Slope in Magnitude Estimation. Journal of Experimental Psychology 71 (2):177.
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