The Log Marginal Likelihood refers to the logarithm of the marginal likelihood function, which is maximized to obtain the optimal set of hyperparameters in Gaussian Process Models. AI generated definition based on: Computer Aided Chemical Engineering, 2018 ...
These latent effects can be used to account for missing predictors or other sources of clustering that could not be explained by a Poisson process. Fitting LGCP models can be challenging because the marginal likelihood does not have a closed form and it involves a high dimensional integral to ...
(KL), Marginal Likelihood (ML), Custom Loss Function (CF), Adversarial Training (AT), Not Available (NA); DL-3: Online (ON), Offline (OFF); DL-4: Supervised (SUP), Semi-supervised (SEMI), Unsupervised (UN); PP-1: Log key (KEY), Token (TOK), Combination (COM); PP-2: Token...
Global markers in class 1 are either synergetic or marginal. In the synergetic case mean vector of each block in class 1 is [1,1/2,⋯,1/k], and in the marginal case it is [1,0,⋯,0]. The covariance matrix of the Gaussian distribution is \sigma ^{2}_{1} \Sigma _{1}. ...
These latent effects can be used to account for missing predictors or other sources of clustering that could not be explained by a Poisson process. Fitting LGCP models can be challenging because the marginal likelihood does not have a closed form and it involves a high dimensional integral to ...
We propose a low- rank continuous-time representation that permits efficient inference in strongly-correlated processes, and obtain variance reduction factors of 8 when using QMC to evaluate the marginal likelihoods, and of 2 when predicting the mean posterior mean and variance when used on ...
For this class, we exploit limiting d-dimensional multivariate Poisson process intensities of the underlying process for inference on all d-vectors exceeding a high marginal threshold in at least one component, employing a censoring scheme to incorporate information below...
Further the log transformation of the data induces a relationship between the mean and the variance of the marginal process. The landmark paper by Diggle et al. (1998), extend the usual Gaussian spatial model to accommodate spatial observations generated by distributions in the exponential family....
We consider the estimation of the marginal likelihood in Bayesian statistics, with primary emphasis on Gaussian graphical models, where the intractability of the marginal likelihood in high dimensions is a frequently researched problem. We propose a general algorithm that can be widely applied to a va...
Under the null hypothesis that the sets of times in the two groups are equivalent, it follows that (𝑑𝑖1), conditional on the marginal total (𝑑𝑖), has a hypergeometric distribution [10,11,12]. Consisting of the sum of (𝑅𝑖1) Bernoulli trials, each with a mean of (𝑑...