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# Flexible conditional density estimators for discrete data
Neural conditional density estimators such as MDNs and MAFs are great for continuous data, but often we run into discrete distributions (…
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**Is your feature request related to a problem? Please describe.**
Mean-shift clustering is a common mode/peak-finding method on imaging and other data, common in classical computer vision problems. …
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## 一言でいうと
ファイナンスの時系列データをニューラルネットワークで予測する際のベストプラクティスについて調査した研究(データの前処理がメイン)。ユーロ・ストックス50指数を題材に十分な予測が行えたとしている。
### 論文リンク
https://arxiv.org/abs/1903.00954
### 著者/所属機関
Jonas Rothfuss, Fabio …
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Raising as part of JOSS review openjournals/joss-reviews#7241
Having an automatic bandwidth selection for conditional density estimation is a very useful feature -- kudos for implementing this. I m…
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Summary:
The "Open Source GIS Tool for Water Harvesting Planning in Indian Cities" aims to develop a user-friendly and accessible GIS tool to support sustainable water management in urban areas acr…
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I took some time to look into @yquilcaille's [`distrib_cov._get_weights_nll`](https://github.com/MESMER-group/mesmer/blob/2b5da4a9f5f450c2b01f8e5e64a97eb35dd99d47/mesmer/mesmer_x/train_l_distrib_mesme…
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add nodes for Kriging, IDW, Kernel Density, or Spatial Temporal Kernel Density Estimation node
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first of all we will define a custom OpenAI Gym environment for our dynamic hedging problem.
Steps to Implement:
Data Collection:
Collecting historical financial news articles and their correspon…
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In #10 I discussed making scatter plots and one of the downsides is the extreme overplotting if entire datasets are used. `ggpointdensity` provides an alternative that is between a scatter plot and a…
Aariq updated
5 months ago
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part of #7338
beta, gamma, invgauss and recipinvgauss kernels can be obtained through sccipy's distributions, with appropriate parameterization.
Birnbaum-Saunders (fatiguelife) should also be pos…