Approximate Bayesian computation
An automatic statistical inference approach using hilbert space embeddings and approximate bayesian computation
... Approximate Bayesian computation was originally introduced as a solution for per- forming statistical inference in the field of molecular biology where systems with high dynamics and stochasticity ...
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Approximate Bayesian Computation in Population Genetics
... for approximate Bayesian statistical inference on the basis of summary ...of Bayesian statistical inference with the computational efficiency of methods based on summary ...
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Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady state signalling simulations
... The computational tools used for forward simulations can also be employed for reverse simulations, i.e., to per- form reverse engineering of experimental data [35] via e.g., Monte Carlo or Bayesian methods, where ...
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WordPress as a framework for automated data capture, filtering and structuring processes
... 2. The other is defined by its heuristic nature; it employs a naive Bayesian classifier Note: In machine learning terminology, the «naive Bayesian» constitutes a family of simple probabi[r] ...
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Bayesian analysis of polarization measurements
... In Bayesian Decision and Estimation Theory determining the “best” estimate of the model parameters requires defining a loss function and a decision rule (there are many good standard texts offering much more detail ...
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Bayesian analysis of textual data
... formal Bayesian multinomial model selection problem and are given a closed form solution, tailored for categorical data and naturally incorporating text length in the ...
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Bayesian network modeling of the consensus between experts: an application to neuron classification
... Second, a clustering algorithm was run on the Bayesian networks to find groups of experts with similar behaviors, and a representative Bayesian network was induced for each cluster of [r] ...
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Thermal food processing computation software
... factors computation, b) computing the process lethality for given process time and vice versa for the case of smooth heating curve; c) computing the process lethality for given process time and vice versa for the ...
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Service Request no 29 -Monthly Data Collection 11 on the current migration in Spain (monthly and thematic focus reports: November, December, January, February, March, April, May, June and July)
... - approximate share of children, families, women, other vulnerable groups; - approximate number of new arrivals applying for asylum in the Member State. - where people arrive by sea, als[r] ...
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TítuloNumerical computation of grounding grids
... Now, for given sets of 2D boundary elements and trial functions de ned on 0, both the earthing electrode surface 0 and the leakage current density can be discretized.. Then, for a given[r] ...
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TítuloEvolutionary computation and QSAR research
... The virtual screening for new molecules with a specific pharmacokinetic activity is the first step in the drug design industry. This computational predictions help to avoid high cost and time-consuming experiments. The ...
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Neuronanatomy, neurology and Bayesian networks
... Multi-dimensional classification for EQ-5D health states from PDQ-39 in Parkinson. Four MBC learning algorithms[r] ...
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Generic communication in parallel computation
... any computation begins, every infinite processors in the machine belong to same group, they execute the same thread and have identical values stored in their local ...
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Prediction in health domain using Bayesian networks optimization based on induction learning techniques
... A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc represents a probabilistic dependency which specifies the conditional probability of each variable given its ...
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Towards Gaussian Bayesian network fusion
... This argument is interesting because it can be compared with the case of huge, noisy data sets, where instead of sub-sampling and learning an individual network, an alternative approa[r] ...
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A. J. Drummond, A. Rambaut, B. Shapiro, and O. G. Pybus - Bayesian skyline plots
... the Bayesian skyline plot (m 5 24) on this data set, the HPD limits of which entirely con- tain the previously published ...The Bayesian sky- line plot is also in complete agreement with known ...
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Good-visibility computation using graphics hardware
... Since good-visibility computation needs to compute the visibility as part of the whole process, the first goal of this PhD thesis is to develop and implement an algorithm for computing th[r] ...
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Efficient computation of the Zassenhaus formula
... In summary, we have presented a new recursive procedure that not only allows us to get the expressions of the Zassenhaus exponents Cn directly in terms of independent commutators in an e[r] ...
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All Path Bridging: Path Exploration Protocols for Data Center and Campus Networks
... Today, link-state routing protocols that compute multiple shortest paths predominate in data center and campus networks, where routing is performed either in layer three or in layer two using link-state routing ...
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Approximate algorithms for decentralized Supply Chain Formation
... Decentralized SCF approaches that employ mediators for goods aim at alleviat- ing part of the responsibility held by the participants in P2P SCF. In order to do so, the concept of local markets is introduced as described ...
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