# Letter B
[*Return*](https://github.com/SyncedAI00/Artificial-Intelligence-Terminology/blob/master/README.md)
索引编号|英文术语|中文翻译|常用缩写|来源&扩展|备注
---|---|---|---|---|---
AITD-00092|Back Propagation|反向传播|BP|[[1]](https://www.jiqizhixin.com/articles/2016-11-25-3)[[2]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[[3]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3)|机器学习|
AITD-00093|Back Propagation Algorithm|反向传播算法||[1]||
AITD-00094|Back Propagation Through Time|随时间反向传播|BPTT|[1]||
AITD-00095|Back-Off|回退||[1]||
AITD-00096|Backward|后向||[1]||
AITD-00097|Backward Induction|反向归纳||[1]||
AITD-00098|Backward Search|反向搜索||[1]||
AITD-00099|Bag of Words|词袋|BOW|[1]||
AITD-00100|Bagging|袋装| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)|机器学习|
AITD-00101|Bandit|赌博机/老虎机||[1]||
AITD-00102|Bandpass Filter|带通滤波器||[1]||
AITD-00103|Base|基||[1]||
AITD-00104|Base Classifier|基分类器||[1]||
AITD-00105|Base Learner|基学习器||[1]||
AITD-00106|Base Learning Algorithm|基学习算法||[1]||
AITD-00107|Base Vector|基向量||[1]||
AITD-00108|Baseline|基准||[[1]](https://www.nature.com/articles/s41557-021-00716-z)|机器学习|
AITD-00109|Basin of Attraction|吸引域||[1]||
AITD-00110|Batch|批量||[1]||
AITD-00111|Batch Gradient Descent|批量梯度下降法|BGD|[1]||
AITD-00112|Batch Learning|批量学习||[1]||
AITD-00113|Batch Normalization|批量规范化|BN|[1]||
AITD-00114|Batch Size|批量大小||[1]||
AITD-00115|Baum-Welch Algorithm|Baum-Welch算法||[1]||
AITD-00116|Bayes Classifier|贝叶斯分类器||[1]||
AITD-00117|Bayes Decision Rule|贝叶斯决策准则||[1]||
AITD-00118|Bayes Error|贝叶斯误差||[1]||
AITD-00119|Bayes Model Averaging|贝叶斯模型平均|BMA|[1]||
AITD-00120|Bayes Optimal Classifier|贝叶斯最优分类器||[1]||
AITD-00121|Bayes Risk|贝叶斯风险||[1]||
AITD-00122|Bayes' Rule|贝叶斯规则||[1]||
AITD-00123|Bayes' Theorem|贝叶斯定理||[1]||
AITD-00124|Bayesian Decision Theory|贝叶斯决策理论||[1]||
AITD-00125|Bayesian Estimation|贝叶斯估计||[1]||
AITD-00126|Bayesian Inference|贝叶斯推断||[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3)|统计，机器学习|
AITD-00127|Bayesian Learning|贝叶斯学习||[1]||
AITD-00128|Bayesian Linear Regression|贝叶斯线性回归||[1]||
AITD-00129|Bayesian Network|贝叶斯网/贝叶斯网络||[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)|Network翻译为网或网络皆可，只要统一翻译成网或者统一翻译成网络即可；统计，机器学习|
AITD-00130|Bayesian Optimization|贝叶斯优化||[[1]](https://www.jiqizhixin.com/articles/2017-11-28)||
AITD-00131|Bayesian Probability|贝叶斯概率||[1]||
AITD-00132|Bayesian Statistics|贝叶斯统计||[1]||
AITD-00133|Beam Search|束搜索||[[1]](https://www.jiqizhixin.com/articles/2018-01-31-3)||
AITD-00134|Benchmark|基准||[1]||
AITD-00135|Belief Network|信念网/信念网络|BN|[1]|Network翻译为网或网络皆可，只要统一翻译成网或者统一翻译成网络即可|
AITD-00136|Belief Propagation|信念传播|BP|[1]||
AITD-00137|Bellman Equation|贝尔曼方程||[1]||
AITD-00138|Bellman Optimality Equation|贝尔曼最优方程||[1]||
AITD-00139|Bernoulli Distribution|伯努利分布||[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)|统计|
AITD-00140|Bernoulli Output Distribution|伯努利输出分布||[1]||
AITD-00141|Best-Arm Problem|最优臂问题||[1]||
AITD-00142|Beta Distribution|贝塔分布||[1]||
AITD-00143|Between-Class Scatter Matrix|类间散度矩阵||[1]||
AITD-00144|BFGS|BFGS||[1]||
AITD-00145|Bi-Directional Long-Short Term Memory|双向长短期记忆|Bi-LSTM|[1]||
AITD-00146|Bi-Partition|二分法||[1]||
AITD-00147|Bias|偏差/偏置||[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[[2]](https://www.nature.com/articles/s41557-021-00716-z)[[3]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3)[[4]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)|看上下语境；机器学习|
AITD-00148|Bias In Affine Function|偏置||[1]|看上下语境|
AITD-00149|Bias In Statistics|偏差||[1]|看上下语境|
AITD-00150|Bias Shift|偏置偏移||[1]||
AITD-00151|Bias-Variance Decomposition|偏差 - 方差分解||[1]||
AITD-00152|Bias-Variance Dilemma|偏差 - 方差困境||[1]||
AITD-00153|Biased|有偏||[[1]](https://www.nature.com/articles/s41557-021-00716-z)|机器学习|
AITD-00154|Biased Importance Sampling|有偏重要采样||[1]||
AITD-00155|Bidirectional Language Model|双向语言模型||[1]||
AITD-00156|Bidirectional Recurrent Neural Network|双向循环神经网络|Bi-RNN|[1]||
AITD-00157|Bigram|二元语法||[1]||
AITD-00158|Bilingual Evaluation Understudy|BLEU||[1]||
AITD-00159|Binary Classification|二分类||[1]||
AITD-00160|Binary Relation|二元关系||[1]||
AITD-00161|Binary Sparse Coding|二值稀疏编码||[1]||
AITD-00162|Binomial Distribution|二项分布||[1]||
AITD-00163|Binomial Logistic Regression Model|二项对数几率回归||[1]||
AITD-00164|Binomial Test|二项检验||[1]||
AITD-00165|Biological Plausibility|生物学合理性||[1]||
AITD-00166|Bit|比特||[1]||
AITD-00167|Block|块||[1]||
AITD-00168|Block Coordinate Descent|块坐标下降||[1]||
AITD-00169|Block Gibbs Sampling|块吉布斯采样||[1]||
AITD-00170|Boilerplate Code|样板代码||[1]||
AITD-00171|Boltzmann|玻尔兹曼||[1]||
AITD-00172|Boltzmann Distribution|玻尔兹曼分布||[1]||
AITD-00173|Boltzmann Factor|玻尔兹曼因子||[1]||
AITD-00174|Boltzmann Machine|玻尔兹曼机||[[1]](https://www.jiqizhixin.com/articles/2017-10-08-4)||
AITD-00175|Boosting|Boosting（一种模型训练加速方式）||[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)||
AITD-00176|Boosting Tree|提升树||[1]||
AITD-00177|Bootstrap Aggregating|Bagging||[1]||
AITD-00178|Bootstrap Sampling|自助采样法||[1]||
AITD-00179|Bootstrapping|自助法/自举法||[1]||
AITD-00180|Bottleneck Layer|瓶颈层||[1]||
AITD-00181|Bottom-Up|自下而上| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)| |
AITD-00182|Bounding Boxes|边界框||[1]||
AITD-00183|Break-Event Point|平衡点|BEP|[1]||
AITD-00184|Bridge Sampling|桥式采样||[1]||
AITD-00185|Broadcasting|广播||[1]||
AITD-00186|Broyden's Algorithm|Broyden类算法||[1]||
AITD-00187|Bucketing|分桶||[1]||
AITD-00188|Burn-In Period|预烧期||[1]||
AITD-00189|Burning-In|磨合||[1]||
AITD-02114|B-Clustering Algorithms|B树聚类算法| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3)|机器学习|
AITD-02115|Balanced Accuracy|平衡精度| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3)|机器学习|
AITD-02116|Bandgap Energy|带隙能量| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)|物理|
AITD-02117|Baseline Test|基准测试| |[[1]](https://www.nature.com/articles/s41557-021-00716-z)|机器学习|
AITD-02118|Basin Hopping|盆地跳跃（算法）| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3)|机器学习|
AITD-02119|Bayesian Approach|贝叶斯方法| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3)|统计，机器学习|
AITD-02120|Bayesian Induction|贝叶斯归纳| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3)|统计，机器学习|
AITD-02121|Bayesian Mcmc Methods|贝叶斯马尔可夫链蒙特卡洛方法| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3)|统计，机器学习|
AITD-02122|Bayesian Methods|贝叶斯方法| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)|统计，机器学习|
AITD-02123|Bayesian Molecular|贝叶斯分子（设计方法）| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3)|统计，机器学习，化学|
AITD-02124|Bayesian Prior|贝叶斯先验| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3)|统计，机器学习|
AITD-02125|Bayesian Program Learning|贝叶斯程序学习|BPL|[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)|统计，机器学习|
AITD-02126|Bayesian Regularized Neural Network|贝叶斯正则化神经网络| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3)|统计，机器学习|
AITD-02127|Beam-Scanning|波束扫描| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3)|物理|
AITD-02128|Best Separates|最优分离| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3)|机器学习|
AITD-02129|Biased Dataset|有偏数据集| |[[1]](https://www.nature.com/articles/s41557-021-00716-z)|机器学习|
AITD-02130|Bit Collisions|字节碰撞/冲突| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3)|数据库|
AITD-02131|Black Box|黑盒子| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3)| |
AITD-02132|Black-Box Attack|黑盒攻击| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3)| |
AITD-02133|Bonding Environments|成键环境| |[[1]](https://www.nature.com/articles/s41557-021-00716-z)| |
AITD-02134|Bonferroni Correction|邦弗朗尼校正| |[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3)|统计|
AITD-02135|Bootstrap Aggregation|引导聚合|bagging|[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)|机器学习|
AITD-02136|Broyden–Fletcher–Goldfarb–Shanno|BFGS（算法）|BFGS|[[1]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3)|一种拟牛顿法，数学计算|
AITD-02137|Buchwald−Hartwig Cross-Coupling|Buchwald–Hartwig 偶联（反应）| |[[1]](https://www.nature.com/articles/s41557-021-00716-z)|化学|
