Results 11 to 20 of about 333,801 (297)
BMC Evolutionary Biology, 2004 Background In phylogenetic analysis we face the problem that several subclade topologies are known or easily inferred and well supported by bootstrap analysis, but basal branching patterns cannot be unambiguously estimated by the usual methods (maximum ...
Rahmann Sven +3 more
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Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles
Frontiers in Molecular Biosciences, 2022 Dynamic behavior of biological systems is commonly represented by non-linear models such as ordinary differential equations. A frequently encountered task in such systems is the estimation of model parameters based on measurement of biochemical compounds.
Tim Litwin +8 more
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Generalised likelihood profiles for models with intractable likelihoods
Statistics and Computing, 2023 Likelihood profiling is an efficient and powerful frequentist approach for parameter estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately, these methods cannot be easily applied for stochastic models without a tractable likelihood function.
David J. Warne +4 more
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Estimating uncertainty of model parameters obtained using numerical optimisation [PDF]
Modeling, Identification and Control, 2019 Obtaining accurate models that can predict the behaviour of dynamic systems is important for a variety of applications. Often, models contain parameters that are difficult to calculate from system descriptions.
Ole Magnus Brastein +3 more
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Bayesian Inference in Extremes Using the Four-Parameter Kappa Distribution
Mathematics, 2020 Maximum likelihood estimation (MLE) of the four-parameter kappa distribution (K4D) is known to be occasionally unstable for small sample sizes and to be very sensitive to outliers.
Palakorn Seenoi +2 more
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Profile likelihood biclustering
Electronic Journal of Statistics, 2020 Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees.
Flynn, Cheryl, Perry, Patrick
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Likelihood-based estimation and prediction for a measles outbreak in Samoa
Infectious Disease Modelling, 2023 Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response. Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise. Furthermore, these
David Wu +4 more
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IEEE Access, 2020 A stochastic scheme, namely, PLM-Lap, has recently been propounded, which relies on the profile likelihood (PL) constructed with a Laplace distribution for estimating muscle activation onsets (MAOs) in surface electromyographic (sEMG) data.
Easter S. Suviseshamuthu +4 more
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Profile Maximum Likelihood Estimation of Single-Index Spatial Dynamic Panel Data Model
Mathematics, 2023 In this paper, the spatial dynamic panel data (SDPD) model is extended to the single-index spatial dynamic panel data (Si-SDPD) model by introducing a nonlinear connection function to reflect the interaction between explanatory variables.
Mengqi Zhang, Boping Tian
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Assessing parameter identifiability for Dynamic Causal Modelling of fMRI data
Frontiers in Neuroscience, 2015 Deterministic dynamic causal modelling (DCM) for fMRI data is a sophisticated approach to analyse effective connectivity in terms of directed interactions between brain regions of interest. To date it is difficult to know if acquired fMRI data will yield
Carolin eArand +6 more
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