资源分类
》 资源 》论文》Z?Transforms and its Inference on Partially Observable Point Processes

Z?Transforms and its Inference on Partially Observable Point Processes

2019-11-05 | |  57 |   38 |   0
Abstract This paper proposes an inference framework based on the Z?transform for a specific class of nonhomogeneous point processes. This framework gives an alternative method to maximum likelihood estimation which is omnipresent in the field of point processes. The inference strategy is to couple or match the theoretical Z?transform with its empirical counterpart from the observed samples. This procedure fully characterizes the distribution of the point process since there exists a one-to-one mapping with the Z?transform. We illustrate how to use the methodology to estimate a point process whose intensity is driven by a general neural network.

上一篇：Open Loop Execution of Tree-Search Algorithms

下一篇：Generalization Bounds for Regularized Pairwise Learning

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...