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A Bayesian approach to latent class models requires specification of prior distributions.
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In contrast, mixed effects models require specification of a specific distribution for the random effects and are less robust to misspecification.
Constructing a BPM model requires specification of a number of microstructural parameters, including the parallel-bond tensile strength, parallel-bond cohesion strength, parallel-bond effective modulus, parallel-bond friction angle, and parallel-bond stiffness ratio.
In addition to these maps, the model requires specification of other inputs that are defined by constants; these are depending on the selected rheology.
Moreover, LCA modeling requires specification of the joint distribution of test results, conditional on disease status.
Simulation of the model required specification of an initial condition, V0 = V 0).
Fundamentally, temperature is a scalar field (i.e. a zeroth order tensor), has no directional information and therefore fails to provide an analogy for functionally grading a 2nd order tensor field (i.e. orthotropic material model) which requires specification of both values and directions throughout the domain.
Likelihood-based inference on joint models requires model specification of the random effects.
These models, however, require specification of numerous input parameters and are typically tuned manually without accounting for uncertainty in the clinical data, hindering their application to large clinical studies.
The FBAT also requires specification of the genetic model.
Maximum likelihood requires specification of a (potentially unrealistic) evolutionary model according to which the sequences are assumed to have evolved.
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