1,430 research outputs found
Inference with Constrained Hidden Markov Models in PRISM
A Hidden Markov Model (HMM) is a common statistical model which is widely
used for analysis of biological sequence data and other sequential phenomena.
In the present paper we show how HMMs can be extended with side-constraints and
present constraint solving techniques for efficient inference. Defining HMMs
with side-constraints in Constraint Logic Programming have advantages in terms
of more compact expression and pruning opportunities during inference.
We present a PRISM-based framework for extending HMMs with side-constraints
and show how well-known constraints such as cardinality and all different are
integrated. We experimentally validate our approach on the biologically
motivated problem of global pairwise alignment
Quantum causal models, faithfulness and retrocausality
Wood and Spekkens (2015) argue that any causal model explaining the EPRB
correlations and satisfying no-signalling must also violate the assumption that
the model faithfully reproduces the statistical dependences and
independences---a so-called "fine-tuning" of the causal parameters; this
includes, in particular, retrocausal explanations of the EPRB correlations. I
consider this analysis with a view to enumerating the possible responses an
advocate of retrocausal explanations might propose. I focus on the response of
N\"{a}ger (2015), who argues that the central ideas of causal explanations can
be saved if one accepts the possibility of a stable fine-tuning of the causal
parameters. I argue that, in light of this view, a violation of faithfulness
does not necessarily rule out retrocausal explanations of the EPRB
correlations, although it certainly constrains such explanations. I conclude by
considering some possible consequences of this type of response for retrocausal
explanations
Inference in Probabilistic Logic Programs with Continuous Random Variables
Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's
PRISM, Poole's ICL, Raedt et al's ProbLog and Vennekens et al's LPAD, is aimed
at combining statistical and logical knowledge representation and inference. A
key characteristic of PLP frameworks is that they are conservative extensions
to non-probabilistic logic programs which have been widely used for knowledge
representation. PLP frameworks extend traditional logic programming semantics
to a distribution semantics, where the semantics of a probabilistic logic
program is given in terms of a distribution over possible models of the
program. However, the inference techniques used in these works rely on
enumerating sets of explanations for a query answer. Consequently, these
languages permit very limited use of random variables with continuous
distributions. In this paper, we present a symbolic inference procedure that
uses constraints and represents sets of explanations without enumeration. This
permits us to reason over PLPs with Gaussian or Gamma-distributed random
variables (in addition to discrete-valued random variables) and linear equality
constraints over reals. We develop the inference procedure in the context of
PRISM; however the procedure's core ideas can be easily applied to other PLP
languages as well. An interesting aspect of our inference procedure is that
PRISM's query evaluation process becomes a special case in the absence of any
continuous random variables in the program. The symbolic inference procedure
enables us to reason over complex probabilistic models such as Kalman filters
and a large subclass of Hybrid Bayesian networks that were hitherto not
possible in PLP frameworks. (To appear in Theory and Practice of Logic
Programming).Comment: 12 pages. arXiv admin note: substantial text overlap with
arXiv:1203.428
05051 Abstracts Collection -- Probabilistic, Logical and Relational Learning - Towards a Synthesis
From 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a Synthesis\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Efficiency in ambiguity: two models of probabilistic semantics for natural language
This paper explores theoretical issues in constructing an adequate probabilistic semantics for natural language. Two approaches are contrasted. The first extends Montague Semantics with a probability distribution over models. It has nice theoretical properties, but does not account for the ubiquitous nature of ambiguity; moreover inference is NP hard. An alternative approach is described in which a sequence of pairs of sentences and truth values is generated randomly. By sacrificing some of the nice theoretical properties of the first approach it is possible to model ambiguity naturally; moreover inference now has polynomial time complexity. Both approaches provide a compositional semantics and account for the gradience of semantic judgements of belief and inference
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