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  1. Simplicity and Model Selection.Guillaume Rochefort-Maranda - 2016 - European Journal for Philosophy of Science 6 (2):261-279.
    In this paper I compare parametric and nonparametric regression models with the help of a simulated data set. Doing so, I have two main objectives. The first one is to differentiate five concepts of simplicity and assess their respective importance. The second one is to show that the scope of the existing philosophical literature on simplicity and model selection is too narrow because it does not take the nonparametric approach into account, S112–S123, 2002; Forster and Sober in The British Journal (...)
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  2. On the Correct Interpretation of P Values and the Importance of Random Variables.Guillaume Rochefort-Maranda - 2016 - Synthese 193 (6):1777-1793.
    The p value is the probability under the null hypothesis of obtaining an experimental result that is at least as extreme as the one that we have actually obtained. That probability plays a crucial role in frequentist statistical inferences. But if we take the word ‘extreme’ to mean ‘improbable’, then we can show that this type of inference can be very problematic. In this paper, I argue that it is a mistake to make such an interpretation. Under minimal assumptions about (...)
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  3. Constructive Empiricism and the Closure Problem.Guillaume Rochefort-Maranda - 2011 - Erkenntnis 75 (1):61-65.
    In this paper I articulate a fictionalist solution to the closure problem that affects constructive empiricism. Relying on Stephen Yablo’s recent study of closure puzzles, I show how we can partition the content of a theory in terms of its truthmakers and claim that a constructive empiricist can believe that all the observable conditions that are necessary to make a part of her theory true obtain and remain agnostic about whether or not the other truthmakers for the other parts of (...)
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  4.  70
    How We Load Our Data Sets with Theories and Why We Do so Purposefully.Guillaume Rochefort-Maranda - 2016 - Studies in History and Philosophy of Science Part A 60:1-6.
    In this paper, I compare theory-laden perceptions with imputed data sets. The similarities between the two allow me to show how the phenomenon of theory-ladenness can manifest itself in statistical analyses. More importantly, elucidating the differences between them will allow me to broaden the focus of the existing literature on theory-ladenness and to introduce some much-needed nuances.
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  5.  12
    Finding True Clusters: On the Importance of Simplicity in Science.Guillaume Rochefort-Maranda & Mo Liu - unknown
    Parametric and dimensional simplicity are not indicators of truth but the methodological principle that urges us to pay attention to such notions of simplicity is truth conducive}. The truth that we are looking for are specific geometrical shapes and we know which algorithm can find which shape provided that we pay attention to parametric and dimensional simplicity.
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  6.  10
    Scientific Evidence, Big Data and the Curse of Dimensionality.Guillaume Rochefort-Maranda - unknown
    The curse of dimensionality is one of the most prominent challenge that data scientists face when trying to make valuable inferences. It is an epistemic problem that hits particularly hard in "Big Data" research contexts, where the volume of the data set is particularly large. The way in which we tackle with this problem sheds light on the notion of scientific evidence. Yet, it is virtually absent from the current philosophical literature. In this paper, I aim to broaden the focus (...)
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  7.  46
    Probabilité Et Support Inductif. Sur le Thèoréme de Popper-Miller (1983).Guillaume Rochefort-Maranda - 2004 - Dialogue 43 (3):499-526.
    In 1983, in an open letter to the journal Nature, Karl Popper and David Miller set forth a particularly strong critical argument which sought to demonstrate the impossibility of inductive probability. Since its publication the argument has faced many criticisms and we argue in this article that they do not reach their objectives. We will first reconstruct the demonstration made by Popper and Miller in their initial article and then try to evaluate the main arguments against it. Although it is (...)
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  8.  8
    Inflated Effect Sizes and Underpowered Tests: How the Severity Measure of Evidence is Affected by the Winner's Curse.Guillaume Rochefort-Maranda - forthcoming - Philosophical Studies.
  9.  8
    Statistical Power and P-Values: An Epistemic Interpretation Without Power Approach Paradoxes.Guillaume Rochefort-Maranda - unknown
    It has been claimed that if statistical power and p-values are both used to measure the strength of our evidence for the null-hypothesis when the results of our tests are not significant, then they can also be used to derive inconsistent epistemic judgements as we compare two different experiments. Those problematic derivations are known as power approach paradoxes. The consensus is that we can avoid them if we abandon the idea that statistical power can measure the strength of our evidence. (...)
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  10.  7
    Simplicity, Truth, and Clustering.Guillaume Rochefort-Maranda - unknown
    Machine learning is a scientific discipline that can be divided into two main branches: supervised machine learning and unsupervised machine learning. In this paper, we aim to show just how simplicity matters in unsupervised contexts. This is important because unsupervised machine learning algorithms have barely received any attention in philosophy. Yet, there is a direct link between simplicity and truth in unsupervised contexts that we do not find in their supervised counterparts. This has thus far evaded philosophical discussions on simplicity.
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  11.  7
    A Paradoxical Feature of the Severity Measure of Evidence.Guillaume Rochefort-Maranda - unknown
    The main point of this paper is to underscore that tests with very low power will be significant only if the observations are deviant under both H0 and H1. Therefore, the results of those significant tests will generate misleadingly high severity scores for differences between H0 and H1 that are excessively overestimated. In other words, that measure of evidence is bound to fail in those cases. It will inevitably fail to adequately measure the strength of the evidence provided by tests (...)
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  12.  7
    The Principle of Total Evidence and Classical Statistical Tests.Guillaume Rochefort-Maranda - unknown
    Classical statistical inferences have been criticised for various reasons. To assess the soundness of such criticisms is a very important task because they are widely used in everyday scientific research. This is one of the reasons why the philosophy of statistics is an exciting field of study. In this paper, I focus on two such criticisms. The first one claims that the use of the p-value violates the principle of total evidence. It is a thesis that has been defended by (...)
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  13.  6
    Frequency-Type Interpretations of Probability in Bayesian Inferences. The Case of MCMC Algorithms.Guillaume Rochefort-Maranda - unknown
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  14.  10
    Probabilité et support inductif. Sur le thèoréme de Popper-Miller.Guillaume Rochefort-Maranda - 2004 - Dialogue 43 (3):499-526.
    ABSTRACT: In 1983, in an open letter to the journal Nature, Karl Popper and David Miller set forth a particularly strong critical argument which sought to demonstrate the impossibility of inductive probability. Since its publication the argument has faced many criticisms and we argue in this article that they do not reach their objectives. We will first reconstruct the demonstration made by Popper and Miller in their initial article and then try to evaluate the main arguments against it. Although it (...)
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  15. La canalisation: Un grand pas pour le philosophe, un petit pour la biologie.Guillaume Rochefort-Maranda - 2003 - Phares 3 (3).
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  16. Pauvreté et aide internationale: Une critique du libéralisme de Peter Bauer.Guillaume Rochefort-Maranda - 2003 - Phares 4 (2).
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