The Bayesian analysis of a state-space model includes computing the posterior distribution of the system’s parameters as well as its latent states. When the latent states wander around ${\mathbb{R}}^n$ there are several well-known modeling components and computational tools that may be profitably combined to achieve this task. When the latent states are constrained to a strict subset of ${\mathbb{R}}^n$ these models and tools are either impaired or break down completely. State-space models whose latent states are covariance matrices arise in finance and exemplify the challenge of devising tractable models in the constrained setting. To that end, we present a state-space model whose observations and latent states take values on the manifold of symmetric positive-definite matrices and for which one may easily compute the posterior distribution of the latent states and the system’s parameters as well as filtered distributions and one-step ahead predictions. Employing the model within the context of finance, we show how one can use realized covariance matrices as data to predict latent time-varying covariance matrices. This approach out-performs factor stochastic volatility.

This article discusses Windle and Carvalho’s (2014) state-space model for observations and latent variables in the space of positive symmetric matrices. The present discussion focuses on the model specification and on the contribution to the positive-value time series literature. I apply the proposed model to financial data with a view to shedding light on some modeling issues.

The article by Windle and Carvalho introduces a fast update procedure for covariance matrices through the introduction of higher frequency sources of information for the underlying process, demonstrated with a financial application. This discussion focuses on outlining the assumptions and constraints around their use in financial applications, as well as an elicitation of some key choices made for comparison with traditional benchmarks, that may ultimately affect the results.

Change point detection models aim to determine the most probable grouping for a given sample indexed on an ordered set. For this purpose, we propose a methodology based on exchangeable partition probability functions, specifically on Pitman’s sampling formula. Emphasis will be given to the Markovian case, in particular for discretely observed Ornstein-Uhlenbeck diffusion processes. Some properties of the resulting model are explained and posterior results are obtained via a novel Markov chain Monte Carlo algorithm.

Splines are useful building blocks when constructing priors on nonparametric models indexed by functions. Recently it has been established in the literature that hierarchical adaptive priors based on splines with a random number of equally spaced knots and random coefficients in the B-spline basis corresponding to those knots lead, under some conditions, to optimal posterior contraction rates, over certain smoothness functional classes. In this paper we extend these results for when the location of the knots is also endowed with a prior. This has already been a common practice in Markov chain Monte Carlo applications, but a theoretical basis in terms of adaptive contraction rates was missing. Under some mild assumptions, we establish a result that provides sufficient conditions for adaptive contraction rates in a range of models, over certain functional classes of smoothness up to the order of the splines that are used. We also present some numerical results illustrating how such a prior adapts to inhomogeneous variability (smoothness) of the function in the context of nonparametric regression.

Theory of graphical models has matured over more than three decades to provide the backbone for several classes of models that are used in a myriad of applications such as genetic mapping of diseases, credit risk evaluation, reliability and computer security. Despite their generic applicability and wide adoption, the constraints imposed by undirected graphical models and Bayesian networks have also been recognized to be unnecessarily stringent under certain circumstances. This observation has led to the proposal of several generalizations that aim at more relaxed constraints by which the models can impose local or context-specific dependence structures. Here we consider an additional class of such models, termed stratified graphical models. We develop a method for Bayesian learning of these models by deriving an analytical expression for the marginal likelihood of data under a specific subclass of decomposable stratified models. A non-reversible Markov chain Monte Carlo approach is further used to identify models that are highly supported by the posterior distribution over the model space. Our method is illustrated and compared with ordinary graphical models through application to several real and synthetic datasets.

Many disparate definitions of Bayesian credible intervals and regions are in use, which can lead to ambiguous presentation of results. It is particularly unsatisfactory when intervals are specified that do not match the one-sided character of the evidence. We suggest that a sensible resolution is to use the parameterization-independent region that maximizes the information gain between the initial prior and posterior distributions, as assessed by their Kullback-Leibler divergence, subject to the constraint on included posterior probability. This turns out to be equivalent to the relative surprise region previously defined by Evans (1997), and thus provides information theoretic support for its use. We also show that this region is the constrained optimizer over the posterior measure of any strictly monotonic function of the likelihood, which explains its many optimal properties, and that it is guaranteed to be consistent with the sidedness of the evidence. Because all of its equivalent derivations depend on the evidence as well as on the posterior distribution, we suggest that it be called the evidentiary credible region.

This paper introduces an extension of the Jeffreys’ rule to the construction of objective priors for non-regular parametric families. A new class of priors based on Hellinger information is introduced as Hellinger priors. The main results establish the relationship of Hellinger priors to the Jeffreys’ rule priors in the regular case, and to the reference and probability matching priors for the non-regular class introduced by Ghosal and Samanta. These priors are also studied for some non-regular examples outside of this class. Their behavior proves to be similar to that of the reference priors considered by Berger, Bernardo, and Sun, however some differences are observed. For the multi-parameter case, a combination of Hellinger priors and reference priors is suggested and some examples are considered.

The Posterior distribution of the Likelihood Ratio (PLR) is proposed by Dempster in 1973 for significance testing in the simple vs. composite hypothesis case. In this hypothesis test case, classical frequentist and Bayesian hypothesis tests are irreconcilable, as emphasized by Lindley’s paradox, Berger & Selke in 1987 and many others. However, Dempster shows that the PLR (with inner threshold 1) is equal to the frequentist p-value in the simple Gaussian case. In 1997, Aitkin extends this result by adding a nuisance parameter and showing its asymptotic validity under more general distributions. Here we extend the reconciliation between the PLR and a frequentist p-value for a finite sample, through a framework analogous to the Stein’s theorem frame in which a credible (Bayesian) domain is equal to a confidence (frequentist) domain.

In stochastic variational inference, the variational Bayes objective function is optimized using stochastic gradient approximation, where gradients computed on small random subsets of data are used to approximate the true gradient over the whole data set. This enables complex models to be fit to large data sets as data can be processed in mini-batches. In this article, we extend stochastic variational inference for conjugate-exponential models to nonconjugate models and present a stochastic nonconjugate variational message passing algorithm for fitting generalized linear mixed models that is scalable to large data sets. In addition, we show that diagnostics for prior-likelihood conflict, which are useful for Bayesian model criticism, can be obtained from nonconjugate variational message passing automatically, as an alternative to simulation-based Markov chain Monte Carlo methods. Finally, we demonstrate that for moderate-sized data sets, convergence can be accelerated by using the stochastic version of nonconjugate variational message passing in the initial stage of optimization before switching to the standard version.