The phrase "augmented likelihood of" is correct and usable in written English.
It can be used in contexts related to statistics, probability, or any field where the likelihood of an event is being increased or enhanced.
Example: "The augmented likelihood of success in this experiment is due to the improved methodology we have implemented."
Alternatives: "increased probability of" or "enhanced chance of".
After 1000 steps, Celecoxib was the most commonly generated structure (about a third of the generated structures), followed by demethylated Celecoxib (also a third) whose SMILES is more likely according to the Prior with (log _e P = -15.2) but has a lower similarity ((J = 0.87)), resulting in an augmented likelihood equal to that of Celecoxib.
For the Agent based on the reduced Prior where (k=0.7), the lower prior likelihood of compounds similar to Celecoxib translates to a lower augmented likelihood, which lowers the average similarity of the structures generated by the Agent.
We therefore denote an augmented likelihood (log P(A)_{mathbb {U}}) as a prior likelihood modulated by the desirability of a sequence: log P(A)_{mathbb {U}} = log P(A)_{Prior} + sigma S A where (sigma) is a scalar coefficient.
The return G(A) of a sequence A can in this case be seen as the agreement between the Agent likelihood (log P(A)_{mathbb {A}}) and the augmented likelihood: G(A) = -[log P(A)_{mathbb {U}} - log P(A)_{mathbb {A}}]^2The goal of the Agent is to learn a policy which maximizes the expected return, achieved by minimizing the cost function (J(Theta ) = -G).
We wish to sample from the joint posterior f θ, Y*∣ Y) of the parameters θ and the latent variables Y* given the data Y, using the fact that, by Bayes' theorem, (7) where L Y*, Y∣θ) is the approximated augmented likelihood.
In Algorithm 2, randEgde returns a random edge from the augmented Set and edgeScore returns the likelihood of the augmented edge.
Conditional on these augmented times and model parameters, the likelihood of the data is available, but since there is no unique way to choose the augmented times given the observation, a systematic exploration of the augmented times is necessary for inference.
The posterior probability of the model, given the data, is Pr (θ | X, Y, S ) ∝ L (θ | X, Y, S ) g = Pr (Y | θ ) Pr (X, S | Y ) g, where L(θ| X, Y, S) is the likelihood of observed and augmented data, given the model parameters, and g is the prior distribution of the model parameters.
Through learning an augmented episodic likelihood which is a composite of prior likelihood [17] and a user defined scoring function, the method aims to fine-tune an RNN pre-trained on the ChEMBL database [20] towards generating desirable compounds.
The total likelihood of the transmission and importation model for a given realization of the augmented data is equal to the product of (equations 1 and (2.
