Computational Methods in Systems Biology
A Scheme for Adaptive Selection of Population Sizes 131 For model selection, the parameterθ, the priorp 0 , as well as the propo ...
132 E. Klinger and J. Hasenauer General form:A density estimateKis expressed as sum of normally distrib- uted kernels K(θ′)= ∑n ...
A Scheme for Adaptive Selection of Population Sizes 133 The score functionSof a scaling factorbcwith densityKcon a sub-populatio ...
134 E. Klinger and J. Hasenauer ab True KDEs n=3 ±2 weighted differential density variation Parameterθ n=50 n= 150 Population si ...
A Scheme for Adaptive Selection of Population Sizes 135 For the inter- and extrapolation ofECV a functional approximationfis use ...
136 E. Klinger and J. Hasenauer 3.1 Appropriateness of the Functional Approximation ofECV for Normal Distributions We first aske ...
A Scheme for Adaptive Selection of Population Sizes 137 05 Generationt 0. 0 0. 1 0. 2 Density variation ECV 0.05 0.1 0.2 010 Gen ...
138 E. Klinger and J. Hasenauer dependence on the number of posterior modes. Since we expected the type of kernel density estima ...
A Scheme for Adaptive Selection of Population Sizes 139 0 20 40 Modelm 1 X Y 0. 0 0. 1 Timet 0 20 40 Modelm 2 Concentrations m 1 ...
140 E. Klinger and J. Hasenauer mean reference posterior. This KS distance increased asECVincreased (Fig. 3 f). The number of re ...
A Scheme for Adaptive Selection of Population Sizes 141 0 10000 Distance 0. 0 0. 5 1. 0 Empirical density × 10 −^3 True MAP 1.0 ...
142 E. Klinger and J. Hasenauer True 0. 32 0. 71 1. 00 1. 41 Relative bandwidthb/bSilv 0. 5 1. 0 Relative bandwidth b/bSilv 0. 0 ...
A Scheme for Adaptive Selection of Population Sizes 143 In the future, the interplay of density estimators and population sizes ...
144 E. Klinger and J. Hasenauer Moral, P.D., Doucet, A., Jasra, A.: An adaptive Sequential Monte Carlo method for Approximate B ...
Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking Kai-Wen Liang^1 ,QinsiWang^1 , Cheryl Telmer^ ...
146 K.-W. Liang et al. In recent years, there has been an increasing effort to automate the process of explaining biological obs ...
Methods to Expand Cell Signaling Models Using Automated Reading 147 Fig. 2.Reading output: (a) Examples of the three types of in ...
148 K.-W. Liang et al. 2.2 New Interaction Classification Often, the computational modelers start with a baseline model, and the ...
Methods to Expand Cell Signaling Models Using Automated Reading 149 Fig. 3.Relationship between reading output and model. (a) Th ...
150 K.-W. Liang et al. Fig. 4.Results from different extension methods: (a) The baseline model and the extensions from automated ...
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