vboost | code supplement for variational boosting

by andymiller Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | vboost Summary

vboost is a Python library. vboost has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However vboost build file is not available. You can download it from GitHub.

code for Variational Boosting: Iteratively Refining Posterior Approximations.

Support

Quality

Security

License

Reuse

Support

vboost has a low active ecosystem.

It has 9 star(s) with 5 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

vboost has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of vboost is current.

Quality

vboost has no bugs reported.

Security

vboost has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

vboost is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

vboost releases are not available. You will need to build from source code and install.

vboost has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed vboost and discovered the below as its top functions. This is intended to give you an instant insight into vboost implemented functionality, and help decide if they suit your requirements.

Make a new component log likelihood function for a given list of components
Compute the covariance matrix
Convert a list of components into a matrices
R Calculate the inverse of the weight matrix
Cholesky decomposition
Compute the low - rank log PDF for the given covariance matrix
Log - likelihood function
Log PDF for log - likelihood
Generate model functions for a given crime
Linear log - density function
Update the gradient of the optimizer
Compute the smoothed gradient of the model
Calculates the log - partition
Make a marginal distribution from a list of matrices
R Converts a standard function to natural scale
R Calculates the inverse of a weight matrix
Generate a random sample from a low rank distribution
R Generate a mixture of TS samples
Convert a set of components to a log probability distribution
Calculates the low - rank KL term using low rank - rank KL
R Calculates the diagonal of a matrix
Process the frisk_with_ noise
Apply smoothing to a parameter
Compute the low - rank log likelihood for a given covariance matrix
Mean Entropy
Plot a joint correlation
Compare the marginal distribution of marginal distribution
Finds the Jacobian of a given vector

Get all kandi verified functions for this library.

vboost Key Features

No Key Features are available at this moment for vboost.

vboost Examples and Code Snippets

No Code Snippets are available at this moment for vboost.

Community Discussions

No Community Discussions are available at this moment for vboost.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install vboost

You can download it from GitHub.
You can use vboost like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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