Motivated by the fact that, in many communication schemes, the achievable transmission rate is determined by a conditional mutual information term, this paper focuses on neural-based estimators for this information-theoretic quantity. Our results are based on variational bounds for the KL-divergence ...
On the other hand, it is shown (Watanabe, 2018b) that WAIC is an asymptotically unbiased estimator of the generalization loss, E[Gn]≅E[Wn], because the property of WAIC is derived from the convergence of the empirical process ξn(u)→ ξ(u) based on the conditional independence of ...
Conditional BRUNO: A Neural Process for Exchangeable Labelled Data conditional density estimationWe present a neural process which models exchangeable sequences of high-dimensional complex observations conditionally on a set of ... I Korshunova,Y Gal,A Gretton,... - 《Neurocomputing》 被引量: 0发...
with density p(∈ ϕ) with nonnegative support, and θ and ϕ are variation free. The baseline intensity, or baseline hazard, is given by (2.13)λ0=p(∈; ϕ)S(∈; ϕ), where S(∈; ϕ)=∫∈∞p(u; ϕ)du is the survivor function. The intensity fun...
In this article, the author has presented a neural network-based conditional density estimator which is semi-non-parametric. In terms of time series modelling, his approach is more general than traditional GARCH models of asset return series because the shape of the conditional density depends on ...
A Neural Bayesian Estimator for Conditional Probability Densities This article describes a robust algorithm to estimate a conditional probability density f(t|x) as a non-parametric smooth regression function. It is based ... M Feindt 被引量: 130发表: 2004年 Estimation Based on Case-Control Design...
E. (1994). Autoregressive conditional density estimation, International Economic Review, 35, 705–730. CrossRef Heyde, C. (1997). Quasi-Likelihood and Its Application: A General Approach to Optimal Parameter Estimation, New York: Springer. Johnson, N. L., S. Kotz, N. Balakrishnan (1994)....
W. (1986). Density estimation for statistics and data analysis. London: Chapman and Hall. Book MATH Google Scholar Sra, S., Nowozin, S., & Wright, S. (2012). Optimization for machine learning. neural information processing series. Cambridge: MIT Press. Google Scholar Sugiyama, M., ...
However, even if gross error occurs in response values, L1 estimator defined as an optimal solution of min w,b 1 m m |yi − f (xi; w, b)|, i=1 16 depresses influence of outliers. Square regularization term is often added to objective functions such as min w,b C m m |yi ...
It is based on a neural network and the Bayesian interpretation of the network output as a posteriori probabability. The network is trained using example events from history or simulation, which define the underlying probability density f(t,x). Once trained, the network is applied on new, ...