bayesian | Utility for Bayesian reasoning | Machine Learning library

I am learning about Dynamic Bayesian Network models using the R package bnlearn. To this end, I am following this paper where they impose certain constraints in the form of 6 layers (Table 1 in the paper):

I’m trying to use PyMC3 Minibatch ADVI for Bayesian Regression. The pm.fit function throws the following error and I’m not sure how to fix it.

It says that the ‘str’ object has no attribute ‘type’. What is any ‘str’ object from the error message here? I’ve mapped float tensors for more_replacements to the best of what I know.

I am attempting to reduce the dimensionality of a categorical feature by extracting an embedding layer from a neural net and using it as an input feature in a separate XGBoost model.

An embedding layer has the dimensions (nr. unique categories + 1, chosen output size). How can it be concatenated to the continuous variables in the original training data with the dimensions (nr. observations, nr. features)?

Below is a reproducible example of regression with a neural net, in which a categorical feature is encoded as a learned embedding layer. The example is closely adapted from: http://machinelearningmechanic.com/keras/2018/03/09/keras-regression-with-categorical-variable-embeddings-md.html#Define-the-input-layers

At the end I have printed the embedding layer and its shape. How can this layer be merged with the continuous features in the original training data (X_train_continuous)? If the number of rows were equal to the number of categories and if we knew the order in which categories are represented in the embedding layer, the embedding array could perhaps be joined to the training observations on category, but instead the number of rows equals the number of categories + 1 (in the code: len(values) + 1).

I am trying to fit a gaussian. I tried to fit using OriginPro and Python. The fit in OriginPro is better than that obtained through Python and I would like to do it using Python.

OriginPro:

Python:

The code I used is:

I receive error messages in R that I can't trace back.

I am trying to fit Bayesian tree models to data with more covariates than observations. With my level of knowledge, I believed this should not be a problem, however, no matter what package I try, all attempts resulted in errors.

When using the bcf()-function from the bcf-package with ncol(x) > nrow(x), I get the error:

bcfoverparRcppClean(yscale[perm], z[perm], t(x_c[perm, ]), t(x_m[perm, : drmu failed

bcfoverparRcppClean() itself is written in C++ which I did not attempt to dig into just yet.

When I only select parts of the covariates, the function performs as expected.

Similar happens for example with the bart()-function from the BayesTree-package. Here, the error reads:

NA/NaN/Inf in foreign function call (arg 7)

Needless to say, that there are no NAs, nor infinite values in the data and the error disappears once more observations than covariates are being fed.

Please find a reproducible example below:

I am trying to optimize the hyperparameters of a LSTM with Bayesian Optimization. But I received the error message TypeError: only integer scalar arrays can be converted to a scalar index when I run the code. A solution I found is to convert the training data and validation data into arrays, but in my code they are already arrays not lists. Or convert them into tuples but I cannot see how I would do this

X_train shape: (946, 60, 1)

y_train shape: (946,)

X_val shape: (192, 60, 1)

y_val shape: (192,)

I am trying to create a table in matplotlib with varied row colors. The colors I provide to ax.table() are not being applied to the cell. I cannot find anything in the documentation that would indicate why. Is this a bug or am I doing something incorrectly?

I am working with Bayesian Networks for the first time and I am using Catnet package in R. The reason being my data is full of categorical variables and a lot of missing data too. I tried the simple example posted elsewhere in StackOverflow just to get a sense of how it works. While it is able to produce the conditional probabilities of each of the nodes in the network, I am not able to visualize the network graphically for an easier understanding. The cnPlot(catNetwork) just returns null. I was wondering if someone with experience using CatNet can point me to the problems with my approach. Thanks.

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