73 citations · 216 across the 15 of their papers we have counts for
11 papers · 1 filter
Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling
Ru He, Jin Tian, Huaiqing Wu
We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to eff…
A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks
Yetian Chen, Jin Tian, Olga Nikolova +1
Exact Bayesian structure discovery in Bayesian networks requires exponential time and space. Using dynamic programming (DP), the fastest known sequential algorithm computes the exa…
Probabilities of Causation: Bounds and Identification
Jin Tian, Judea Pearl
This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structural-semantical definitions of the probabilit…
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
Jin Tian
This paper extends the work in [Suzuki, 1996] and presents an efficient depth-first branch-and-bound algorithm for learning Bayesian network structures, based on the minimum descri…
Causal Discovery from Changes
Jin Tian, Judea Pearl
We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the underlying data-generating model. We analyze the classes of st…
On the Testable Implications of Causal Models with Hidden Variables
Jin Tian, Judea Pearl
The validity OF a causal model can be tested ONLY IF the model imposes constraints ON the probability distribution that governs the generated data. IN the presence OF unmeasured va…