New to Bayesian network, my question is to how to use conditional probability distribution in each node with directed acyclic graph
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I have taken a few deeplearning.ai courses, all of which focus on fully-connected network topologies which are all interconnected while neglecting sparsely connected ones.
I am wondering if I can use Tensorflow to calculate both the forward and backward propagation of a sparse neural network, like the following:
I'm assuming traditional matrix operations won't work for this type of topology, since not all nodes are connected equally.
I have been using standard packages for survival analysis in R. I know how to do classification problems in TensorFlow such as logistic regression, but I am having difficulty mapping this to survival analysis problems. In a way, instead of one output vector you have two (time_to_event::continuous, censored::boolean). This has been done in Theano, here, but I am having difficulty translating this to TensorFlow.
You can use a logistic regression to do the survival analysis, however, another way you can use TensorFlow is to have the tf model predict the parameters of a survival distribution. So if you used the Weibull distribution you could, instead of regressing onto the time to event and a censoring probability, estimate the characteristic life (alpha parameter) and the shape (beta parameter). That is, the tf model estimates the parameters of the survival distribution directly.
The loss function can be the maximum likelihood which means you can incorporate observed and censored data.
Is the TensorFlow designed only for implementing neural networks? Can it be used as a general machine learning library -- for implementing all sorts of supervised as well as unsupervised techniques (naive baysian, decision trees, k-means, SVM to name a few) ? Whatever TensorFlow literature I am coming across is generally talking about neural networks. Probably graph based architecture of TensorFlow makes it suitable candidate for neural nets. But can it be also used as a general Machine Learning framework?
Tensorflow does include additional machine learning algorithms such as:
K-means clustering
Random Forests
Support Vector Machines
Gaussian Mixture Model clustering
Linear/logistic regression
The above list is taken from here, so you can read this link for more details.
I have read that convolution networks scale very well across multiple gpus, but what about deep neural networks that don't use convolutions? The Tensorflow website provides a multiple gpu example, but it uses convolutions.
If I understand it right, both use Bayes Theorem to generate an acyclic graph and calculate percentages based on functions applied at every node.
What is the difference?
One simple and fundamental difference is
Acyclic Graph != Tree
For example, a->b<-c is not a tree (it has two roots), but it is an acyclic graph.
I am not well versed in decision trees, but I am well versed in Bayesian Networks.
Here are some things that you can do with Bayesian Networks that I am not sure if you can do with a decision tree. Researching how to do these things with a decision tree may reveal interesting differences.
Compute the joint probability table between the variables
Determine if two variables are conditionally independent
Given some evidence, determine the distribution of the non-evidence variables given the evidence