We describe how graphical Markov models started to emerge in the last 40 years, based on
three essential concepts that had been developed independently more than a century ago …

Ordered sequences of univariate or multivariate regressions provide statistical models for
analysing data from randomized, possibly sequential interventions, from cohort or multi …

A set of independence statements may define the independence structure of interest in a
family of joint probability distributions. This structure is often captured by a graph that …

In this paper, we define and study the concept of traceable regressions and apply it to some
examples. Traceable regressions are sequences of conditional distributions in joint or single …

We introduce and study a subclass of joint Bernoulli distributions which has the palindromic
property. For such distributions the vector of joint probabilities is unchanged when the order …

Undetected confounding may severely distort the effect of an explanatory variable on a
response variable, as defined by a stepwise data-generating process. The best known type …

Graphical Markov models combine conditional independence constraints with graphical
representations of stepwise data generating processes. The models started to be formulated …

We introduce and study distributions of sets of binary variables that are symmetric, that is
each has equally probable levels. The joint distribution of these special types of binary …

To observe and understand relations among several features of individuals or objects is one
of the central tasks in many substantive fields of research, including the medical, social …

Graphical Markov models are multivariate statistical models which are currently under
vigorous development and which combine two simple but most powerful notions, generating …