Question 1

How to fit a Hidden Markov Model with Gaussian emissions in Dynamax?

Accepted Answer

Use the GaussianHMM class: initialize with k-means, then call fit_em for expectation-maximization, as demonstrated in the example code. This provides smoothed probabilities and parameter estimates efficiently.

Question 2

Dynamax vs TensorFlow Probability for state space models: which is better?

Accepted Answer

Dynamax is JAX-native and optimized for performance with GPU acceleration, making it faster for large-scale SSMs. TensorFlow Probability offers broader probabilistic modeling but may be slower; choose Dynamax if you're committed to the JAX ecosystem and need high speed.

Question 3

Does Dynamax support non-Gaussian observation models?

Accepted Answer

Yes, it includes Generalized Gaussian State Space Models for non-Gaussian emissions, such as Poisson or binomial distributions, though implementation details may require referencing the documentation and tutorials for specific use cases.

Question 4

Can I run Dynamax on GPU for faster inference?

Accepted Answer

Absolutely, since it's built on JAX, you can easily enable GPU support by installing JAX with CUDA, allowing for accelerated computations in inference and learning tasks, especially with batch operations via vmap.

Question 5

What are the best practices for parameter estimation in Dynamax?

Accepted Answer

Combine EM for quick fitting with Gaussian models or use stochastic gradient descent via optax for large datasets. For Bayesian approaches, integrate with Blackjax for MCMC/SMC, as highlighted in the README's extension examples.

Question 6

How scalable is Dynamax for high-dimensional time-series data?

Accepted Answer

It scales well due to JAX's vectorization and GPU support, but performance can degrade with very high-dimensional states or emissions; optimize by batching sequences and leveraging vmap, as shown in the batch operations example.

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