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To estimate the model parameters, the most common approach is to optimize a likelihood function, which quantifies how good the model explains the data as a function of its parameters [75].
To achieve this, we partition the genome into overlapping, contiguous windows of SNPs, and we optimize a likelihood model over each of the windows.
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The method of obtaining estimates of the unknown parameters of the LMM was by optimizing a likelihood function.
In addition, it estimates isoform and gene expression (fragments per kilobase exon per million mapped fragments, or FPKM) by optimizing a likelihood function containing transcript abundances as parameters.
The main algorithmic idea of ProbHap is a new dynamic programming algorithm that exactly optimizes a likelihood function specified by a probabilistic graphical model and which generalizes a popular objective called the minimum error correction.
The existence of the latent variables z prevents to optimize a complete likelihood function defined using Eq. (4).
We develop an efficient learning algorithm, based on generalized expectation maximization (EM), to optimize a bound on the likelihood termed free energy (Neal and Hinton, 1998).
In addition, a soft decision tree construction algorithm optimizing a log-likelihood measure is developed.
The spatial smoothing parameter for rate estimation is optimized using a likelihood comparison approach.
Model parameters were optimized to a likelihood difference of 0.00001.
In this model, we use the Restricted Maximum Likelihood (ReML) method to optimize a Bayesian random-effects model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com