In this section, we derive a simple Gaussian moments algorithm for noisy unifying model when the noise covariance matrix is known, and a new Gaussian moments�...
Yumin Yang, Chonghui Guo: Gaussian moments for noisy unifying model. Neurocomputing 71(16-18): 3656-3659 (2008). manage site settings.
In the network model below, the smaller circles represent noise sources and all units are linear. Outgoing weights have only been drawn from one hidden unit.
The first is that we assume the data has consistent zero-mean Gaussian measurement noise. How sensitive the loss function and heuristic are to outliers and�...
... noisy unifying model when the noise covariance matrix is known. Next, when the noise covariance is unknown, a new Gaussian moments algorithm is developed.
Part 1. SEQUENCE MODELS. 27. Chapter 2. The Multivariate Normal Distribution. 28. 2.1. Linear Regression and sequence models.
In this article we are going to consider two of the most basic time series models, namely White Noise and Random Walks.
The basic models we will work with are discrete time linear dynamical systems with Gaussian noise. In such models we assume that the state of the process in�...
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Dec 13, 2020 � One option would be to model x as arising from, e.g., a Poisson distribution with mean parameter z, where log(z) follows a Gaussian distribution�...
Missing: moments | Show results with:moments
This paper presents the first review of noise models in classification covering both label and attribute noise. Their study reveals the lack of a unified�...
Missing: moments | Show results with:moments
