Humans are creatures of habits. They tend to repeat their behaviors. Neural network techniques are powerful tools that can be used to extract such behaviors from historical data and as such can predict where the user is expected to go next if we model his behavior correctly. In our experiments, we compare results of the proposed Bayesian Neural Network with 5 standard neural network techniques in predicting both next location and next service to request. Bayesian learning for Neural Networks predicts both location and service better than standard neural network techniques since it uses well founded probability model to represent uncertainty about the relationship being learned. The result of Bayesian training is a posterior distribution over network weights. We use Markov chain Monte Carlo methods (MCMC) to sample N values from the posterior weights distribution. These N samples vote for the best prediction.
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机译：人类是习惯的生物。他们倾向于重复他们的行为。神经网络技术是强大的工具，可用于从历史数据中提取此类行为，因此如果我们正确模拟他的行为，可以预测用户预期接下来的位置。在我们的实验中，我们将提出的贝叶斯神经网络的结果与5个标准神经网络技术进行了预测，预测下一个位置和下一个服务请求。对于神经网络的贝叶斯学习预测了比标准神经网络技术更好的位置和服务，因为它使用了很好的概率模型来代表所学习关系的不确定性。贝叶斯训练的结果是网络权重的后部分布。我们使用Markov Chain Monte Carlo方法（MCMC）来从后重量分布中采样n值。这些n样品投票给最好的预测。
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