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A Study of Bayesian Inference in Medical DiagnosisHerzig, Michael 05 1900 (has links)
<p> Bayes' formula may be written as follows: </p> <p> P(yᵢ|X) = P(X|yᵢ)・P(yᵢ)/j=K Σ j=1 P(X|yⱼ)・P(yⱼ) where (1) </p> <p> Y = {y₁, y₂,..., y_K} </p> <P> X = {x₁, x₂,..., xₖ} </p> <p> Assuming independence of attributes x₁, x₂,..., xₖ, Bayes' formula may be rewritten as follows: </p> <p> P(yᵢ|X) = P(x₁|yᵢ)・P(x₂|yᵢ)・...・P(xₖ|yᵢ)・P(yᵢ)/j=K Σ j=1 P(x₁|yⱼ)・P(x₂|yⱼ)・...・P(xₖ|yⱼ)・P(yⱼ) (2) </p> <p> In medical diagnosis the y's denote disease states and the x's denote the presence or absence of symptoms. Bayesian inference is applied to medical diagnosis as follows: for an individual with data set X, the predicted diagnosis is the disease yⱼ such that P(yⱼ|X) = max_i P(yᵢ|X), i=1,2,...,K (3) </p> <p> as calculated from (2). </p> <p> Inferences based on (2) and (3) correctly allocate a high proportion of patients (>70%) in studies to date, despite violations of the independence assumption. The aim of this thesis is modest, (i) to demonstrate the applicability of Bayesian inference to the problem of medical diagnosis (ii) to review pertinent literature (iii) to present a Monte Carlo method which simulates the application of Bayes' formula to distinguish among diseases (iv) to present and discuss the results of Monte Carlo experiments which allow statistical statements to be made concerning the accuracy of Bayesian inference when the assumption of independence is violated. </p> <p> The Monte Carlo study considers paired dependence among attributes when Bayes' formula is used to predict diagnoses from among 6 disease categories. A parameter which measured deviations from attribute independence is defined by DH=(j=6 Σ j=1|P(x_B|x_A,yⱼ)-P(x_B|yⱼ)|)/6, where x_A and x_B denote a dependent attribute pair. It was found that the correct number of Bayesian predictions, M, decreases markedly as attributes increasing diverge from independence, ie, as DH increases. However, a simple first order linear model of the form M = B₀+B₁・DH does not consistently explain the variation in M. </p> / Thesis / Master of Science (MSc)
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研究Ferguson-Dirichlet過程和條件分配族相容性之新工具 / New tools for studying the Ferguson-Dirichlet process and compatibility of a family of conditionals郭錕霖, Kuo,Kun Lin Unknown Date (has links)
單變量c-特徵函數已被證明可處理一些難以使用傳統特徵函數解決的問題，
在本文中，我們首先提出其反演公式，透過此反演公式，我們獲得（1）Dirichlet隨機向量之線性組合的機率密度函數；（2）以一些有趣測度為參數之Ferguson-Dirichlet過程其隨機動差的機率密度函數；（3）Ferguson-Dirichlet過程之隨機泛函的Lebesgue積分表示式。
本文給予對稱分配之多變量c-特徵函數的新性質，透過這些性質，我們證明在任何$n$維球面上之Ferguson-Dirichlet過程其隨機均值是一對稱分配，並且我們亦獲得其確切的機率密度函數，此外，我們將這些結果推廣至任何n維橢球面上。
我們亦探討條件分配相容性的問題，這個問題在機率理論與貝式計算上有其重要性，我們提出其充要條件。當給定相容的條件分配時，我們不但解決相關聯合分配唯一性的問題，而且也提供方法去獲得所有可能的相關聯合分配，我們亦給予檢驗相容性、唯一性及建構機率密度函數的演算法。
透過相容性的相關理論，我們提出完整且清楚地統合性貝氏反演公式理論，並建構可應用於一般測度空間的廣義貝氏反演公式。此外，我們使用廣義貝氏反演公式提供一個配適機率密度函數的演算法，此演算法沒有疊代演算法（如Gibbs取樣法）的收斂問題。 / The univariate c-characteristic function has been shown to be important in cases that are hard to manage using the traditional characteristic function. In this thesis, we first give its inversion formulas. We then use them to obtain (1) the probability density functions (PDFs) of a linear combination of the components of a Dirichlet random vector; (2) the PDFs of random functionals of a Ferguson-Dirichlet process with some interesting parameter measures; (3) a Lebesgue integral expression of any random functional
of the Ferguson-Dirichlet process.
New properties of the multivariate c-characteristic function with a spherical distribution are given in this thesis. With them, we show that the random mean of a Ferguson-Dirichlet process over a spherical surface in n dimensions has a spherical distribution on the n-dimensional ball. Moreover, we derive its exact PDF. Furthermore, we generalize this result to any ellipsoidal surface in n-space.
We also study the issue of compatibility for specified conditional distributions. This issue is important in probability theory and Bayesian computations. Several necessary and sufficient conditions for the compatibility are provided. We also address the problem of uniqueness of the associated joint distribution when the given conditionals are compatible. In addition, we provide a method to obtain all possible joint distributions that have the given compatible conditionals. Algorithms for checking the compatibility and the uniqueness, and for constructing all associated densities are also given.
Through the related compatibility theorems, we provide a fully and cleanly unified theory of inverse Bayes formula (IBF) and construct a generalized IBF (GIBF) that is applicable in the more general measurable space. In addition, using the GIBF, we provide a marginal density fitting algorithm, which avoids the problems of convergence in iterative algorithm such as the Gibbs sampler.
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Information on a default time : Brownian bridges on a stochastic intervals and enlargement of filtrations / Information sur le temps de défaut : ponts browniens sur des intervalles stochastiques et grossissement de filtrationsBedini, Matteo 12 October 2012 (has links)
Dans ce travail de thèse le processus d'information concernant un instant de défaut τ dans un modèle de risque de crédit est décrit par un pont brownien sur l'intervalle stochastique [0, τ]. Un tel processus de pont est caractérisé comme plus adapté dans la modélisation que le modèle classique considérant l'indicatrice I[0,τ]. Après l'étude des formules de Bayes associées, cette approche de modélisation de l'information concernant le temps de défaut est reliée avec d'autres informations sur le marché financier. Ceci est fait à l'aide de la théorie du grossissement de filtration, où la filtration générée par le processus d'information est élargie par la filtration de référence décrivant d'autres informations n'étant pas directement liées avec le défaut. Une attention particulière est consacrée à la classification du temps de défaut par rapport à la filtration minimale mais également à la filtration élargie. Des conditions suffisantes, sous lesquelles τ est totalement inaccessible, sont discutées, mais également un exemple est donné dans lequel τ évite les temps d'arrêt, est totalement inaccessible par rapport à la filtration minimale et prévisible par rapport à la filtration élargie. Enfin, des contrats financiers comme, par exemple, des obligations privée et des crédits default swaps, sont étudiés dans le contexte décrit ci-dessus. / In this PhD thesis the information process concerning a default time τ in a credit risk model is described by a Brownian bridge over the random time interval [0, τ]. Such a bridge process is characterised as to be a more adapted model than the classical one considering the indicator function I[0,τ]. After the study of related Bayes formulas, this approach of modelling information concerning the default time is related with other financial information. This is done with the help of the theory of enlargement of filtration, where the filtration generated by the information process is enlarged with a reference filtration modelling other information not directly associated with the default. A particular attention is paid to the classification of the default time with respect to the minimal filtration but also with respect to the enlarged filtration. Sufficient conditions under which τ is totally inaccessible are discussed, but also an example is given of a τ avoiding the stopping times of the reference filtration, which is totally inaccessible with respect to its own filtration and predictable with respect to the enlarged filtration. Finally, common financial contracts like defaultable bonds and credit default swaps are considered in the above described settings.
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