# Tensorflow Probability vs. Pymc3: Which is Better?

Tensorflow Probability and Pymc3 are two of the most popular libraries for statistical modeling and machine learning. But which is better?

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## Introduction

In this article, we’ll be comparing two of the most popular probabilistic programming libraries: Tensorflow Probability and Pymc3. We’ll discuss the pros and cons of each library, and help you decide which one is right for your needs.

## What is Tensorflow Probability?

TensorFlow Probability is an open-source Python library designed to make it easy to apply probabilistic reasoning and statistical analysis in TensorFlow programs. Its main purpose is to make it easier to get started with probabilistic programming, and to enable researchers and developers to build on top of it.

TensorFlow Probability combines the strengths of both TensorFlow and Theano to provide a powerful tool for working with probabilistic models. It makes it easy to define probability distributions, manipulate them, and compute expectations over them. It also provides tools for automatically constructing probability models from data, and for visualizing and debugging them.

TensorFlow Probability is released under an Apache 2.0 license, and its development is led by the Google Brain team.

## What is Pymc3?

Pymc3 is a library for probabilistic programming in Python that allows users to flexibly specify statistical models to perform Bayesian inference. Pymc3 has been developed since 2010 by a large team of collaborators and is currently led by Andrew Cron. The developers of Pymc3 have taken care to design a user-friendly API that makes it easy to get started with probabilistic programming.

Pymc3 is GPL licensed and freely available on GitHub.

## Key Differences between Tensorflow Probability and Pymc3

There are a few key differences between Tensorflow Probability (TFP) and Pymc3 that should be considered when deciding which library to use for probabilistic modeling.

TFP is a toolkit for probabilistic programming that is built on top of Tensorflow. It allows you to define probabilistic models and then perform inference on those models. TFP also has the ability to scale models up to very large datasets, making it a good choice for those who need to work with big data.

Pymc3, on the other hand, is a pure Python library for probabilistic programming. It does not require Tensorflow and can be used with any numerical computing library. Pymc3 also has features that make it easier to prototype models and perform Bayesian inference.

## When to Use Tensorflow Probability?

Tensorflow Probability is a powerful tool for statistical analysis and machine learning. However, it can be difficult to know when to use it and when to use another tool like Pymc3. In this article, we will compare Tensorflow Probability and Pymc3, and help you decide which is the better option for your needs.

## When to Use Pymc3?

Tensorflow Probability (TFP) is a great tool for probabilistic modeling and inference, but it can be challenging to get started with. Pymc3 is a popular Python library for probabilistic programming that makes it easy to get started with Bayesian modeling. In this article, we’ll compare TFP and Pymc3, and discuss when you might want to use each one.

Tensorflow Probability is a powerful tool for probabilistic modeling, but it can be challenging to get started with. If you’re looking for an easy-to-use library for probabilistic programming, Pymc3 is a good option. Pymc3 makes it easy to get started with Bayesian modeling, and it also has advanced features for more complex models.

When deciding whether to use TFP or Pymc3, there are a few things to consider:

– Ease of use: If you’re just getting started with probabilistic programming, Pymc3 is a good choice because it’s easy to use. TFP is more powerful but can be challenging to get started with.
– Model complexity: TFP can handle more complex models than Pymc3. If you’re building a very complex model, TFP may be a better choice.
– Inference: Both TFP and Pymc3 offer built-in methods for inference (e.g., Markov Chain Monte Carlo), but TFP offers more flexibility and customizability.

In general, TFP is a good choice if you’re looking for a powerful tool for probabilistic modeling, while Pymc3 is a good choice if you’re looking for an easy-to-use library for Bayesian modeling.

## Pros and Cons of Tensorflow Probability

Tensorflow Probability (TFP) is a powerful tool for statistical modeling and machine learning. It is easy to use and has a wide variety of applications. However, there are some drawbacks that you should be aware of before using TFP.

One of the biggest advantages of TFP is that it is easy to use. You don’t need to be a statistician or machine learning expert to use it. In addition, TFP is flexible and can be used for a variety of tasks.

However, there are some disadvantages to using TFP. One downside is that it can be slow. In addition, TFP can be difficult to debug and there is limited documentation available.

## Pros and Cons of Pymc3

Pymc3 is a Python library for probabilistic programming that allows you to build complex models and conduct Bayesian inference. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine statistical and machine learning models, perform inference, and estimate uncertainty.

So, which one should you use? Here we will compare the pros and cons of Pymc3 and TFP to help you decide which one is right for you.

Pymc3 Pros:
-Allows for construction of complex models
-Flexible architecture- can use GPU or CPU for computation
-Can perform most types of Bayesian inference

Pymc3 Cons:
-Steeper learning curve than TFP
-Not as widely used as TFP, so less community support

TFP Pros:
-Easier to learn than Pymc3
-More widely used than Pymc3, so more community support
-Can perform most types of Bayesian inference

## Conclusion

After comparing the two libraries side-by-side, it’s clear that there is no definitive answer to which is better. Both have their pros and cons, and the best way to decide is to try them out for yourself. If you’re just getting started with probabilistic programming, TensorFlow Probability might be a good place to start because of its integrations with other TensorFlow libraries. If you’re more experienced, PyMC3 might be a better choice because of its flexibility. In any case, whichever library you choose, you’ll be able to build powerful models that can help you unlock the hidden insights in your data.