PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games " Method 3: Modify the/etc/environment file Increase the path setting, add the following at the end:Įxport PATH=$PATH :/usr/local/arm/4.4.3/bin Method 2: Modify the/etc/profile file (this file is a system-level environment variable, The things set in it are applicable to all users) Methods ) Method 1: Modify the/etc/bash.bashrc file (this file is only applicable to the current user)Īdd at the end export PATH=$PATH:/opt/FriendlyARM/toolschain/4.4.3/bin (path) (note that there is no space on the equal sign) (I have only used the first method to do it, and I have not tried the other Some people have tried it on UBUNTU 11.10 and they are feasible. There are a total of three methods below. There are many ways to modify environment variables on the Internet. Sudo cp -r/opt/FriendlyARM/toolschain/4.4.3/usr/local/arm Executing this command will install arm-linux-gcc to the/opt/Friendlyarm/toolschain/4.4.3 directory. Note: there is a space after C (not required), and C is uppercase (required), it is the first letter of the English word "Change", in This is the meaning of changing the directory. Sudo tar xvzf arm-linux-gcc-4.4.3.tgz -C/ 目录Ĩ.After decompression directly, -C/will automatically place the decompressed files in the specified path in the root directory, don’t worry)ĭownload arm-linux-gcc-4.4.3.tgz to any directory and enter this folder Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs. Most chapters also include boxes with additional material: skill boxes, which describe techniques case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology and concept boxes, which present significant concepts drawn from the material in the chapter. The main text in each chapter provides the detailed technical development of the key ideas. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. Most tasks require a person or an automated system to reason-to reach conclusions based on available information.
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