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Practical Probabilistic Programming.pdf

概率编程

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Practical Probabilistic Programming.pdf 由manning出版社出版，对应中文版翻译书为：《概率编程实战》。是概率编程入门绝佳的好资料。该书介绍了什么是概率编程，和一个概率编程系统Figaro，该框架基于scala，易于扩展，支持多数常用概率推理算法。

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Avi Pfeffer

OREWORD

Stuart Russell

Practical Probabilistic Programming

Practical

Probabilistic

Programming

FEFFER

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HELTER

SLAND

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2019-02-02

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if self.n_future > 0: present_state = states[:, :1].contiguous() if self.cfg.PROBABILISTIC.ENABLED: # Do probabilistic computation sample, output_distribution = self.distribution_forward( present_state, future_distribution_inputs, noise ) output = {output, output_distribution} # Prepare future prediction input b, _, _, h, w = present_state.shape hidden_state = present_state[:, 0] if self.cfg.PROBABILISTIC.ENABLED: future_prediction_input = sample.expand(-1, self.n_future, -1, -1, -1) else: future_prediction_input = hidden_state.new_zeros(b, self.n_future, self.latent_dim, h, w) # Recursively predict future states future_states = self.future_prediction(future_prediction_input, hidden_state) # Concatenate present state future_states = torch.cat([present_state, future_states], dim=1)这段代码得到什么？

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Machine learning: a probabilistic perspective. Cambridge, MA: MIT press. 4. 网络资料：作者/组织. (年份). 文章/网页标题. 检索日期, 来源. 例如： Scikit-learn. (2021). Decision Trees. Retrieved May...

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class FuturePrediction(torch.nn.Module): def init(self, in_channels, latent_dim, n_gru_blocks=3, n_res_layers=3): super().init() self.n_gru_blocks = n_gru_blocks # Convolutional recurrent model with z_t as an initial hidden state and inputs the sample # from the probabilistic model. The architecture of the model is: # [Spatial GRU - [Bottleneck] x n_res_layers] x n_gru_blocks self.spatial_grus = [] self.res_blocks = [] for i in range(self.n_gru_blocks): gru_in_channels = latent_dim if i == 0 else in_channels self.spatial_grus.append(SpatialGRU(gru_in_channels, in_channels)) self.res_blocks.append(torch.nn.Sequential([Bottleneck(in_channels) for _ in range(n_res_layers)])) self.spatial_grus = torch.nn.ModuleList(self.spatial_grus) self.res_blocks = torch.nn.ModuleList(self.res_blocks) def forward(self, x, hidden_state): # x has shape (b, n_future, c, h, w), hidden_state (b, c, h, w) for i in range(self.n_gru_blocks): x = self.spatial_grus[i](x, hidden_state, flow=None) b, n_future, c, h, w = x.shape x = self.res_blocks[i](x.view(b n_future, c, h, w)) x = x.view(b, n_future, c, h, w) return x是什么意思？

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Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566-580. 2. LaValle, S. M. (2006). Planning algorithms. ...

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