BayesFusion Support Forum

# How to observe variables, set_evidence() on large network and update_beliefs()

I want to observe several variables and update_belifes() and then check the posterior probability of the outcome variable of interest.

I am write code as follows
```
net.set_evidence(node_name, val)
net.update ...

I have a causal graph with the following edges : W -> X, X -> T, W -> T, A -> T, B -> T, B -> A. All of the node, W, X, A, B, T are binary categorical variable with value 0 and 1. I have simulated the dataset in the following way:


def simulate_dataset(N, seed):
"""
N: Sample size
"""
np ...

"In SMILE, the single CPT can have 2^31 elements - with all binary nodes this would be a node with 30 parents."

Is this a hard limit, OR is it possible to extend this limit somehow?

I got an error when creating a network using the code below:



from functools import partial
import pysmile
import pysmile_license


def create_cpt_node(net, id, name, outcomes, x_pos, y_pos):

handle = net.add_node(pysmile.NodeType.CPT, id)

net.set_node_name(handle, name)
net.set_node ...

I want to use this method
read_pandas_dataframe(self: pysmile.learning.DataSet, *args, **kwargs) -> None
to create DataSet object directly from a pandas dataframe.

Could you please tell me how can I fix this issue? Thanks.
The sample code is below:


# %%
import pandas as pd
import pysmile
import ...

Thank you for your information for setting seed using network properties.

Before, I was setting seed using the following code, which does not work.

```
random.seed(seed)
np.random.seed(seed)
```

I tried just now using method `pysmile.Network.set_rand_seed`

```
model.set_rand_seed(seed)
```

I ...

Dear Community,

I am following the PDF version of the tutorial for SMILE wrappers, specifically for python language.

I am try SMILE package on our real-world dataset.

Since the real world dataset is too big. I have to use "pysmile.BayesianAlgorithmType.EPIS_SAMPLING" to produce inference ...

Hi,

"set explicit targets in the network if you're only interested in the subset of node probabilities"

print_posteriors() function is like below:

def print_posteriors(net, node_handle):
node_id = net.get_node_id(node_handle)
if net.is_evidence(node_handle):
print(f"{node_id} has evidence ...

Dear Community,

I am following the PDF version of the tutorial for SMILE wrappers, specifically for python language.


I am trying SMILE package on our real-world dataset.

Our dataset has 1654 nodes, 2965 edges.
Among the 1654 nodes, there are 605 continuous variables and 1049 categorical ...

Hi, I have a following up question:

Does SMILE support inference on hybrid network?
Suppose we learn the parameter in some other way. Can we encode the learned parameter in the hybrid network and then we do inference or manipulation of variables on it?

For instance, there is a case a discrete ...

Thanks, I have read "GeNIe manual, chapter 'Controlling values'". it is clear to me now.

Can I ask a further question?
I search both GeNIe manual and Wrapper manual.
I did not find the document to introduce how to use the function `Network.set_controlled_value`.

Could you please instruct me ...

We want to use SMILE to do manipulation for a variable, and see how the manipulation can affect the target variable.

Here is a brief explanation of the difference between "observing" and "manipulation/intervening".

Given a graph B -> A, A -> T, B -> T.

In Tutorial 2, if we use function `change ...

Dear Support team,

I am following the PDF version of the tutorial for SMILE wrappers, specifically for the Python language.

Is there a way to output goodness-of-fit metric such as BIC, log likelihood score, given a predefined network structure and a dataset?

BIC score (Bayesian Information ...

Dear Support team,

I am following the PDF version of the tutorial for SMILE wrappers, specifically for the Python language.

I tried to simulate a hybrid network (with both discrete and continuous variables).

I first predefined a network structure and then simulated a dataset. I tried to use ...

Dear Community,

I am following the PDF version of the tutorial for SMILE wrappers, specifically for the Python language.

To simulate a dataset from a hybrid Bayesian network with both categorical and continuous variables, we need to:

Define the conditional probability tables (CPTs) for ...