Source: Deep Learning on Medium

Why traditional probabilistic modeling focused on capture linear relationships among random variables.

The seminal works of Judea Pearl [@Pearl88] and Stephen Lauritzen [@lauritzen1992propagation] about probabilistic graphical models (PGMs) placed probabilistic modeling as an indispensable tool for dealing with many problems involving any form of uncertainty within many different fields such as artificial intelligence [@russell2016artificial], statistics [@HastieTibshiraniFriedman01], machine learning [@bishop2006pattern; @murphy2012machine], etc. PGMs has been present for the last 30 years becoming a well established and highly influential body of research.

At the same time, the inference problem [@Pearl88; @lauritzen1992propagation; @JensenNielsen07; @koller2009probabilistic], as the problem of computing the posterior probability over hidden quantities given the known evidence, has been the corner-stone (and the bottleneck) of the feasibility and applicability of probabilistic modeling.

In the beginning, the first proposed inference algorithms [@Pearl88; @lauritzen1992propagation] were able to compute this posterior in an exact way by exploiting the conditional independence relationships encoded by the graphical structure of the model. Even though, model’s probability distributions were strongly restricted mainly to multinomial and (conditional linear) Gaussian distributions [@JensenNielsen07; @koller2009probabilistic]. But researchers quickly realized these exact inferences schemes were not powerful enough to deal with complex stochastic dependency structures that arise in many relevant problems. Mainly due to the high computational costs associated to the inference algorithms [@koller2009probabilistic]. In consequence, approximated inference methods started to be the main focus of research.

Monte-Carlo methods were one of the first approximate methods employed to make inference over complex PGMs [@gilks1995markov; @plummer2003jags]. They are extremely powerful and able to approximate complex posterior distributions. However, they have serious issues like problems of convergence of the underlying Markov chain, poor mixing, etc. when having to approximate highly dimensional posteriors [@gilks1995markov]. And computing these highly-dimensional posteriors started to be relevant in many domains, specially when researchers seek to apply a Bayesian approach to learn the parameters of their PGMs from data [@bishop2006pattern; @murphy2012machine; @blei2014build]. In this case, the model parameters are treated as unobserved random variables, and the learning problem reduces to compute the posterior probability over them. For models with a large number of parameters results in highly dimensional posteriors where the application of Monte-Carlo methods became infeasible. And these issues gave rises to the development of alternative approximate inference schemes.

Belief propagation (BP) [@Pearl88; @murphy1999loopy], and the close scheme called Expectation propagation (EP) [@minka2001expectation], has been successfully used in many applications of PGMs helping to overcome many of the limitations of Monte-Carlo methods. They are approximate deterministic inference techniques which can be implemented using a message-passing scheme which exploits the graph structure of the PGM and, hence, the underlying conditional independence relationships among variables. In terms of distributional assumptions, BP was mainly restricted to multinomial and Gaussian distributions, while EP allows for a more general family of distributions, although restricted by the need to define a non-trivial quotient operation between the involved densities. As already commented, these techniques (a many variations also published later) are deterministic and overcame some of the difficulties of Monte-Carlo methods. However they presented two main issues: they did not guarantee the convergence to an approximate and meaningful solution; and did not scale to the kind of models that appear in the context of Bayesian learning (i.e. plateau like models) [@murphy2012machine; @blei2014build]. Again, these issues motivated researchers to look into in alternative approximate inference schemes.

Variational methods [@wainwright2008graphical] were firstly explored in the late 90s [@jordan1999introduction], inspired by their successful application in inference problems encountered in statistical physics. They are deterministic approximate inference techniques like BP and EP methods. Their main innovation comes from casting the inference problem as the problem of maximizing a well defined loss function (i.e. the ELBO function) acting as an inference proxy. In general, variational methods guarantee convergence to a local minimum of this ELBO function, and, then, to a meaningful solution. By transforming the inference problem in a continuous optimization problem, variational methods could take advantage of recent advances in continuous optimization theory. That was the case of the widely adopted stochastic gradient descent algorithm [@bottou2010large], which was successfully used by the machine learning community to scale their learning algorithms to big data sets. This same learning algorithm was adapted to the variational inference problem [@JMLR:v14:hoffman13a], giving the opportunity to apply probabilistic modeling approaches to problems involving massive data sets. But, in terms of distributional assumptions, VI methods were tightly restricted to the conjugate exponential family [@barndorff2014information], where ELBO’s gradients can be computed in closed-form [@WinnBishop05]. Ad-hoc approaches were developed over the years for non-conjugate models.

Then, since the start of the field at end of the eighties, probabilistic models has been mainly focused on exploiting conditional independencies among random variables and modelling the dependencies using distributions belonging to the conjugate exponential family. But exponential family distributions are only able to model linear relationships between the random variables [@WinnBishop05]. The recent success of deep learning [@goodfellow2016deep] has been partly due to the capacity of deep neural networks to model highly non-linear relationships among highly-dimensional objects as happens between the pixels of an image or the words of a document, to name just the most known examples.

Recent advances in variational inference [@kingma2013auto; @ranganath2014black] gave the opportunity to introduce in probabilistic models deep neural networks to capture non-linear relationships among random variables, giving rise to a whole new family of probabilistic models, which are mainly known as *deep generative models* [@hinton2009deep; @hinton2012practical; @goodfellow2014generative; @salakhutdinov2015learning], which is a very active field of research. This new family of probabilistic models are able to model in a probabilistic manner objects like images, text, audio, video, etc. in a much powerful manner than before, by bringing to the probabilistic modeling field many of the recent advances produced by the deep learning community. The release of modern probabilistic programming languages [@tran2016edward; @cabanasInferPy; @tran2018simple; @bingham2018pyro] relying on well established deep learning engines [@hinton2009deep; @hinton2012practical; @goodfellow2014generative; @salakhutdinov2015learning] are also greatly expanding the adoption of these powerful probabilistic modeling techniques.

### References

Barndorff-Nielsen, Ole. 2014. *Information and Exponential Families: In Statistical Theory*. John Wiley & Sons.

Bingham, Eli, Jonathan P Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D Goodman. 2018. “Pyro: Deep Universal Probabilistic Programming.” *arXiv Preprint arXiv:1810.09538*.

Bishop, Christopher M. 2006. *Pattern Recognition and Machine Learning*. springer.

Blei, David M. 2014. “Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models.” *Annual Review of Statistics and Its Application* 1: 203–32.

Bottou, Léon. 2010. “Large-Scale Machine Learning with Stochastic Gradient Descent.” In *Proceedings of Compstat’2010*, 177–86. Springer.

Cabañas, Rafael, Antonio Salmerón, and Andrés R. Masegosa. 2019. “InferPy: Probabilistic Modeling with Tensorflow Made Easy.” *Knowledge-Based Systems*.

Gilks, Walter R, Sylvia Richardson, and David Spiegelhalter. 1995. *Markov Chain Monte Carlo in Practice*. Chapman; Hall/CRC.

Goodfellow, Ian, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. *Deep Learning*. Vol. 1. MIT press Cambridge.

Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” In *Advances in Neural Information Processing Systems*, 2672–80.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2001. *The Elements of Statistical Learning*. New York, NY, USA: Springer New York Inc.

Hinton, Geoffrey E. 2009. “Deep Belief Networks.” *Scholarpedia* 4 (5): 5947.

— — — . 2012. “A Practical Guide to Training Restricted Boltzmann Machines.” In *Neural Networks: Tricks of the Trade*, 599–619. Springer.

Hoffman, Matthew D., David M. Blei, Chong Wang, and John Paisley. 2013. “Stochastic Variational Inference.” *Journal of Machine Learning Research* 14: 1303–47.

Jensen, Finn V., and Thomas D. Nielsen. 2007. *Bayesian Networks and Decision Graphs*. Berlin, Germany: Springer-Verlag.

Jordan, Michael I, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. 1999. “An Introduction to Variational Methods for Graphical Models.” *Machine Learning* 37 (2): 183–233.

Kingma, Diederik P, and Max Welling. 2013. “Auto-Encoding Variational Bayes.” *arXiv Preprint arXiv:1312.6114*.

Koller, Daphne, and Nir Friedman. 2009. *Probabilistic Graphical Models: Principles and Techniques*. MIT press.

Lauritzen, Steffen L. 1992. “Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models.” *Journal of the American Statistical Association* 87 (420): 1098–1108.

Minka, Thomas P. 2001. “Expectation Propagation for Approximate Bayesian Inference.” In *Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence*, 362–69. Morgan Kaufmann Publishers Inc.

Murphy, Kevin P. 2012. *Machine Learning: A Probabilistic Perspective*. MIT press.

Murphy, Kevin P, Yair Weiss, and Michael I Jordan. 1999. “Loopy Belief Propagation for Approximate Inference: An Empirical Study.” In *Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence*, 467–75. Morgan Kaufmann Publishers Inc.

Pearl, Judea. 1988. *Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference*. San Mateo, CA.: Morgan Kaufmann Publishers.

Plummer, Martyn, and others. 2003. “JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling.” In *Proceedings of the 3rd International Workshop on Distributed Statistical Computing*. Vol. 124. 125.10. Vienna, Austria.

Ranganath, Rajesh, Sean Gerrish, and David Blei. 2014. “Black Box Variational Inference.” In *Artificial Intelligence and Statistics*, 814–22.

Russell, Stuart J, and Peter Norvig. 2016. *Artificial Intelligence: A Modern Approach*. Malaysia; Pearson Education Limited,

Salakhutdinov, Ruslan. 2015. “Learning Deep Generative Models.” *Annual Review of Statistics and Its Application* 2: 361–85.

Tran, Dustin, Matthew W Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, and Alexey Radul. 2018. “Simple, Distributed, and Accelerated Probabilistic Programming.” In *Advances in Neural Information Processing Systems*, 7608–19.

Tran, Dustin, Alp Kucukelbir, Adji B Dieng, Maja Rudolph, Dawen Liang, and David M Blei. 2016. “Edward: A Library for Probabilistic Modeling, Inference, and Criticism.” *arXiv Preprint arXiv:1610.09787*.

Wainwright, Martin J, Michael I Jordan, and others. 2008. “Graphical Models, Exponential Families, and Variational Inference.” *Foundations and Trends in Machine Learning* 1 (1–2): 1–305.

Winn, John M., and Christopher M. Bishop. 2005. “Variational Message Passing.” *Journal of Machine Learning Research* 6: 661–94.

*Originally published at **gist.github.com**.*