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  1. Malcolm R. Forster, I. A. Kieseppä, Dan Hausman, Alexei Krioukov, Stephen Leeds, Alan Macdonald & Larry Shapiro (forthcoming). The Conceptual Role of 'Temperature'in Statistical Mechanics: Or How Probabilistic Averages Maximize Predictive Accuracy. Philosophy of Science.
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  2. I. A. Kieseppä (2003). AIC and Large Samples. Philosophy of Science 70 (5):1265-1276.
    I discuss the behavior of the Akaike Information Criterion in the limit when the sample size grows. I show the falsity of the claim made recently by Stanley Mulaik in Philosophy of Science that AIC would not distinguish between saturated and other correct factor analytic models in this limit. I explain the meaning and demonstrate the validity of the familiar, more moderate criticism that AIC is not a consistent estimator of the number of parameters of the smallest correct model. I (...)
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  3. I. A. Kieseppä (2003). AICand Large Samples. Philosophy of Science 70 (5):1265-1276.
    I discuss the behavior of the Akaike Information Criterion in the limit when the sample size grows. I show the falsity of the claim made recently by Stanley Mulaik in Philosophy of Science that AIC would not distinguish between saturated and other correct factor analytic models in this limit. I explain the meaning and demonstrate the validity of the familiar, more moderate criticism that AIC is not a consistent estimator of the number of parameters of the smallest correct model. I (...)
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  4. I. A. Kieseppä (2003). Akaike's Theorem and Bayesian Methodology. In. In A. Rojszczak, J. Cachro & G. Kurczewski (eds.), Philosophical Dimensions of Logic and Science. Kluwer Academic Publishers. 117--137.
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  5. I. A. Kieseppá (2002). Stadler, Friedrich: The Vienna Circle. Studies in Theorigins, Development, and Influence of Logicalempiricism. [REVIEW] Journal for General Philosophy of Science 33 (2):409-416.
    Reviews the book "The Vienna Circle. Studies in the Origins, Development, and Influence of Logical Empiricism," by Friedrich Stadler.
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  6. I. A. Kieseppa (2001). Statistical Model Selection Criteria and Bayesianism. Philosophy of Science 68 (3):S141 - S152.
    Two Bayesian approaches to choosing between statistical models are contrasted. One of these is an approach which Bayesian statisticians regularly use for motivating the use of AIC, BIC, and other similar model selection criteria, and the other one is a new approach which has recently been proposed by Bandyopadhayay, Boik, and Basu. The latter approach is criticized, and the basic ideas of the former approach are presented in a way that makes them accessible to a philosophical audience. It is also (...)
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  7. I. A. Kieseppä (2001). Statistical Model Selection Criteria and the Philosophical Problem of Underdetermination. British Journal for the Philosophy of Science 52 (4):761 - 794.
    I discuss the philosophical significance of the statistical model selection criteria, in particular their relevance for philosophical problems of underdetermination. I present an easily comprehensible account of their simplest possible application and contrast it with their application to curve-fitting problems. I embed philosophers' earlier discussion concerning the situations in which the criteria yield implausible results into a more general framework. Among other things, I discuss a difficulty which is related to the so-called subfamily problem, and I show that it has (...)
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  8. I. A. Kieseppä (2001). Statistical Model Selection Criteria and Bayesianism. Proceedings of the Philosophy of Science Association 2001 (3):S141 - S152.
    Two Bayesian approaches to choosing between statistical models are contrasted. One of these is an approach which Bayesian statisticians regularly use for motivating the use of AIC, BIC, and other similar model selection criteria, and the other one is a new approach which has recently been proposed by Bandyopadhayay, Boik, and Basu. The latter approach is criticized, and the basic ideas of the former approach are presented in a way that makes them accessible to a philosophical audience. It is also (...)
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  9. I. A. Kieseppa (2001). Statistical Model Selection Criteria and the Philosophical Problem of Underdetermination. British Journal for the Philosophy of Science 52 (4):761-794.
    I discuss the philosophical significance of the statistical model selection criteria, in particular their relevance for philosophical of underdetermination. I present an easily comprehensible account of their simplest possible application and contrast it with their application to curve-fitting problems. I embed philosophers' earlier discussion concerning the situations in which the criteria yield implausible results into a more general framework. Among other things, I discuss a difficulty which is related to the so-called subfamily problem, and I show that it has analogies (...)
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  10. I. A. Kieseppä (2000). Rationalism, Naturalism, and Methodological Principles. Erkenntnis 53 (3):337-352.
    The nature of the distinction between rational andnon-rational accounts of the development of science isanalyzed. These two kinds of accounts differ mostlyin the status which they give to methodologicalprinciples. It is shown that there are severaldimensions with respect to which the status of suchprinciples can resemble more or less the kind ofstatus that a paradigmatic rational account would givethem. It is concluded that, under the most plausibledefinitions of a rational account, the extent to whicha philosophical account of scientific change isrational (...)
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  11. I. A. Kieseppä (1997). Akaike Information Criterion, Curve-Fitting, and the Philosophical Problem of Simplicity. British Journal for the Philosophy of Science 48 (1):21-48.
    The philosophical significance of the procedure of applying Akaike Information Criterion (AIC) to curve-fitting problems is evaluated. The theoretical justification for using AIC (the so-called Akaike's theorem) is presented in a rigorous way, and its range of validity is assessed by presenting both instances in which it is valid and counter-examples in which it is invalid. The philosophical relevance of the justification that this result gives for making one particular choice between simple and complicated hypotheses is emphasized. In addition, recent (...)
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  12. I. A. Kieseppä (1996). On the Aim of the Theory of Verisimilitude. Synthese 107 (3):421 - 438.
    J. P. Z. Bonilla's methodological approach to truthlikeness is evaluated critically. On a more general level, various senses in which the theory of truthlikeness could be seen as a theory concerned with methodology are distinguished, and it is argued that providing speical sciences with methodological tools is unrealistic as an aim of the theory of verisimilitude. Rather, when developing this theory, one should rest contnet with the more modest aim of conceptual analysis, or of providing explications for the relational concept (...)
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  13. I. A. Kieseppä (1996). Truthlikeness for Hypotheses Expressed in Terms of N Quantitative Variables. Journal of Philosophical Logic 25 (2):109 - 134.
    A qualitative theory of truthlikeness, based on a family of quantitative measures, is developed for hypotheses that are concerned with the values of a finite number of real-valued quantities. Representing hypotheses by subsets of $R^{n}$ , I first show that a straightforward application of the basic ideas of the similarity approach to truthlikeness does not work out for hypotheses with zero n-dimensional Lebesgue measure. However, it is easy to give a counterpart for the average measure preferred by Pavel Tichý and (...)
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