Multivariate Modality Inference Using Gaussian Kernel
نویسندگان
چکیده
The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its asymptotic distribution are derived to assess the significance of each mode. The inference procedure is applied on both simulated and real data sets.
منابع مشابه
Kernel Methods for Nonparametric Bayesian Inference of Probability Densities and Point Processes
Nonparametric kernel methods for estimation of probability densities and point process intensities have long been of interest to researchers in statistics and machine learning. Frequentist kernel methods are widely used, but provide only a point estimate of the unknown density. Additionally, in frequentist kernel density methods, it can be difficult to select appropriate kernel parameters. The ...
متن کاملNonparametric Bayesian inference on multivariate exponential families
We develop a model by choosing the maximum entropy distribution from the set of models satisfying certain smoothness and independence criteria; we show that inference on this model generalizes local kernel estimation to the context of Bayesian inference on stochastic processes. Our model enables Bayesian inference in contexts when standard techniques like Gaussian process inference are too expe...
متن کاملKernel Embeddings of Conditional Distributions
Many modern applications of signal processing and machine learning, ranging from computer vision to computational biology, require the analysis of large volumes of high-dimensional continuous-valued measurements. Complex statistical features are commonplace, including multi-modality, skewness, and rich dependency structures. Such problems call for a flexible and robust modeling framework that c...
متن کاملThe Multivariate Generalised von Mises: Inference and Applications
Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending some standard probabilistic modelling tools to the circular domain. First we introduce a new multivariate distribution over circular variables, called t...
متن کاملAsymmetric kernel in Gaussian Processes for learning target variance
This work incorporates the multi-modality of the data distribution into a Gaussian Process regression model. We approach the problem from a discriminative perspective by learning, jointly over the training data, the target space variance in the neighborhood of a certain sample through metric learning. We start by using data centers rather than all training samples. Subsequently, each center sel...
متن کامل