The Bi-Gaussian Likelihood Model
Pseudo-Likelihood Learning of Gaussian Graphical Models
... marginal likelihood of any Gaussian DAG ...of Gaussian Directed Acyclic Graphical models in their article ...to Gaussian DAG model comparison is based on using Bayes factors and ...
61
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
... a Gaussian or binary distribution in such a way that the resulting undirected graphical model is ...maximum likelihood problem with an added ℓ 1 -norm penalty ...maximum likelihood problem for ...
35
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
... We can make some tentative observations by browsing the network of senators. As neighbors most Democrats have only other Democrats and Republicans have only other Republicans. Senator Chafee (R, RI) has only Democrats as ...
32
Maximum likelihood estimation for Gaussian process with nonlinear drift
... centered Gaussian process. We construct the maximum likelihood estimators of the drift parameter θ based on discrete and continuous observations of the process X and prove their strong ...the model ...
21
Robust Gaussian Process Regression with a Student-t Likelihood
... Student-t model depends heavily on the data and the ...inflexible model) can result into a very large number of outliers because the model is unable to explain large quantity of the obser- ...
31
Text dependent Forensic Voice Comparison: Likelihood Ratio Estimation with the Hidden Markov Model (HMM) and Gaussian Mixture Model
... Markov Model (HMM) and the Gaussian Mixture Model (GMM), for the calculation of forensic LRs using the same speech ...for model training and LR estima- tion. Log-likelihood-ratio cost ...
9
Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables
... SAL-Negbin model version that, as explained above, only requires minor modi cations of the baseline spatial-EIS ...Poisson model, as further con rmed by a large log-likelihood dierence of ...
42
Maximum likelihood estimation of robust constrained Gaussian mixture models
... highest likelihood values. The spatial layout model and the constraints defined in sections ...individual Gaussian components to rotate around their centroids while preserving the rel- ative ...
189
Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance
... Second, the indirect inference technique calibrates the bias function via simulation and hence does not require, just like the jackknife method, an explicit form for the bias function or its expansion. Consequently, the ...
35
Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes
... the model and the subset of parameters ...APARCH model requires the estimation of a parameter that is the power to which we should model the volatility ...
35
A Convex Approach for Image Restoration with Exact Poisson-Gaussian Likelihood
... Poisson-Gaussian model can accurately describe the noise present in a number of imaging ...continuous-discrete model corresponding to the data ...
22
Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes
... Our second argument is related to Figure 2 and Figure 3. The first one provides the news im- pact curve obtained for each model with the different sets of estimated parameters. This concept introduced in the ...
34
Maximum likelihood and Gaussian estimation of continuous time models in finance
... Indirect inference has several advantages for estimating continuous time models. First, it overcomes the inconsistency problem that is common in many approximate ML methods. Second, the indirect inference technique ...
34
Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance
... The II estimation procedure essentially builds in a small-sample bias correction to parameter estimation, with the bias (in the base estimate, like ML) being computed directly by simulation. Indirect inference has ...
36
Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance
... The II estimation procedure essentially builds in a small-sample bias correction to parameter estimation, with the bias (in the base estimate, like ML) being computed directly by simulation. Indirect inference has ...
35
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
... graph model induced by the graph G in Figure 5(i) has a deviance of ...good model fit; the p-value computed using a chi-square distribution is ...maximum likelihood estimate obtained by fitting the ...
22
A Convex Approach for Image Restoration with Exact Poisson-Gaussian Likelihood
... can observe that a high number of low intensity components are lost when using a data fidelity term derived from Poisson statistics. Similarly, the shape of low intensity components is not well reconstructed when using a ...
22
Generalized Likelihood Ratio Test for Detection of Gaussian Rank-One Signals in Gaussian Noise With Unknown Statistics
... target model. The exact generalized likelihood ratio test was de- rived and was shown to bear close resemblance to its determin- istic counterpart, Kelly’s ...
12
Sensitivity analysis of likelihood ratio test in K distributed and/or Gaussian noise
... the Gaussian assumption prevails for thermal noise, in a number of appli- cations, such as low grazing angles or with sea clutter, the heavy-tail nature of clutter has been observed experimentally ...
6
Maximum likelihood estimation of Gaussian mixture models using stochastic search
... t Gaussian mixture models (GMM), commonly used in pattern recognition and machine learning, provide a flexible probabilistic model for the ...maximum likelihood estimation of the parameters of GMMs is ...
13