LogLikelihood for Gaussian Mixture ModelsAlfred UltschCatharina Lippmann
示例8: negative_log_likelihood ▲点赞 1▼ defnegative_log_likelihood(self, y):""" Return the mean of the negativelog-likelihood of the prediction of this model under a given target distribution. .. math:: \frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =...
It has been shown that the authentication performance of a biometric system is dependent on the models/templates specific to a user. As a result, some users may be more easily recognised or impersonated than others. We propose amodel-specific(or user-specific) likelihood based score normalisation...
Updated GridSearchCV to include scoring=None for utilizing the default log-likelihood scoring of GaussianMixture, ensuring appropriate evaluation during cross-validation.
In order to find the proper number of lags for the VAR (and also to the BVAR), a Log likelihood ratio – test (LR-test) is done with results from Schwarz and Akaike criteria and it suggests just one lag, as seen in the Table 7 below. Table 7. Likelihood Ratio (BR) No Lags H0...
The inaccuracies appear in the function _estimate_log_gaussian_prob.The log-probabilities can be off by 0.2 (see the very last example), which really is not small if those probabilities are used for likelihoods.I uploaded the full script at https://github.com/JohannesBuchner/gmm-tests/blob/...
To solve the inherent non-adaptive problem existed in the expected patch Log likelihood (EPLL), an updating process of the Gaussian mixture model introduced into the EPLL and an improved EPLL scheme via adaptive Gaussian mixture prior is proposed in this paper. Experimental results show that the ...
A new log-likelihood (LL) based metric for goodness-of-fit testing and monitoring unsupervised learning of mixture densities is introduced, called differential LL. We develop the metric in the case of a Gaussian kernel fitted to a Gaussian distribution. We suggest a possible differential LL learni...
Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In the context of a Gaussian Mixture Model (GMM), this idea has been shown to lead to state-of-the-art results in image...
We use the theory of high dimensional model representation (HDMR) to build interpretable low dimensional approximations of the log-likelihood ratio accounting for the effects of each individual gene as well as gene-gene interactions. We propose two algorithms approximating the second order HDMR ...