Sentence examples for the augmented likelihood from inspiring English sources

Starting from a Prior network trained on ChEMBL, the Agent is trained using the augmented likelihood of the SMILES generated.

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).

Then according to (7) the posterior distribution for the unknown (Θ, M g *, P* is given by where we approximate L M g, P, M g *, P*∣θ) with the augmented likelihood in (4) for small sampling intervals for all observed and auxiliary data, i.e. y=(M g, P, M g *, P*).

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.

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.

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=1), the fact that Celecoxib and demethylated Celecoxib are given similar augmented likelihoods means that the average similarity converges to around 0.9 rather than 1.0.

In Algorithm 2, randEgde returns a random edge from the augmented Set and edgeScore returns the likelihood of the augmented edge.

The model was tested on the task of generating sulphur free molecules as a proof of principle, and the method using augmented episodic likelihood was compared with traditional policy gradient methods.

The observation model determines the likelihood of the observed data (the patient swabs) for a given realization of the epidemic process (the augmented data), and the transmission and importation model specifies the likelihood of the realization given the model parameters.