Exact(3)
We compute the evidence and posterior probability for each of three possible hypotheses to describe a pair of curves.
Analysis of Gaussian likelihood functions indicates that 25 active points is sufficient to compute the evidence integral in up to 30 dimensions.
For comparison we have included results from Nested Sampling [ 14, 35] as a means to perform the integration and compute the evidence.
Similar(57)
Nested Sampling was used to compute the evidences.
To compute the evidences we need to integrate the likelihood, P(D| ω, σ, c, H n, I) from equation (8), over ω, σ, and c for each model H n. By assigning proper normalised priors to all model parameters it is possible to integrate over them around the maximum likelihood estimate.
The values used in computing the evidence were α ̂ = 2. 8, =20.
Nested sampling generates a sequence of posterior samples from the parameter space as a by-product of computing the evidence integral.
After estimating all models and their evidence (the negative free energy expressed here as a log-evidence), we computed the group evidence (of 90 models over 60 subjects) separately for word or picture modulation using the BMS procedure.
After estimating all models and their evidence (the negative free energy F expressed here as a log evidence), we then computed the group evidence (of 27 models over 28 subjects) using the BMS procedure.
Deduction uses the complementary equation P(Evidence|Hypothesis) to compute the likelihood of evidence given a hypothesis.
After estimating all 33 models for each participant, we computed the group evidence for all models using random effects (RFX) BMS as implemented in DCM10.
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