Markov logic networks | SpringerLink
... the second equality above shows. This will be the most convenient approach in domains with a mixture of hard and soft constraints (i.e., where some formulas hold with certainty, leading to zero probabilities for some worlds). The graphical structure of ML,C follows from Definition 4.1: there is an e ...
... the second equality above shows. This will be the most convenient approach in domains with a mixture of hard and soft constraints (i.e., where some formulas hold with certainty, leading to zero probabilities for some worlds). The graphical structure of ML,C follows from Definition 4.1: there is an e ...
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... their leaves, but with semantics quite different from standard search trees. The typical semantics given ...
... their leaves, but with semantics quite different from standard search trees. The typical semantics given ...
Representing Probabilistic Rules with Networks of
... only two ways to understand the functionality of the network: by plotting patterns of weight values or by gathering statistics of the network output through extensive play. The former method provides no more than a general impression; the latter forces the human to redo the entire learning process. ...
... only two ways to understand the functionality of the network: by plotting patterns of weight values or by gathering statistics of the network output through extensive play. The former method provides no more than a general impression; the latter forces the human to redo the entire learning process. ...
Knowledge Discovery System For Cost
... characteristics, often make selection of materials for a given component a difficult task. If the selection process is made haphazardly, there will be a risk of overlapping possible attractive and alternative materials. The risk can be reduced by designing expert systems with artificial intelligent ...
... characteristics, often make selection of materials for a given component a difficult task. If the selection process is made haphazardly, there will be a risk of overlapping possible attractive and alternative materials. The risk can be reduced by designing expert systems with artificial intelligent ...
CS 188: Artificial Intelligence Today Uncertainty Probabilities
... Probability that it’s warm AND sunny? Probability that it’s warm? ...
... Probability that it’s warm AND sunny? Probability that it’s warm? ...
I A Sensitivity Analysis of Pathfinder
... dence that discriminates among them. Thus, at any point in time, Pathfinder's partial diagnosis ...
... dence that discriminates among them. Thus, at any point in time, Pathfinder's partial diagnosis ...
Probabilistic ODE Solvers with Runge-Kutta Means
... Calibration of uncertainty A question easily posed but hard to answer is what it means for the probability distribution returned by a probabilistic method to be well calibrated. For our Gaussian case, requiring RK order in the posterior mean determines all but one degree of freedom of an answer. The ...
... Calibration of uncertainty A question easily posed but hard to answer is what it means for the probability distribution returned by a probabilistic method to be well calibrated. For our Gaussian case, requiring RK order in the posterior mean determines all but one degree of freedom of an answer. The ...
Mininw Mlrltivzarid-e Time C&w
... We are concerned with regression tasks for which the potential design matrix dimensionality is typically in the hundreds or more, due to large numbers of raw sensors and basis function transforms (e.g. time-lagged values and time-windowed mins and maxs). Despite the relative cheapnessof linear regre ...
... We are concerned with regression tasks for which the potential design matrix dimensionality is typically in the hundreds or more, due to large numbers of raw sensors and basis function transforms (e.g. time-lagged values and time-windowed mins and maxs). Despite the relative cheapnessof linear regre ...
Inference in Bayesian Networks
... So, in a domain with four variables, A, B, C, and D, the probability that variable D has value d is the sum over all possible combinations of values of the other three variables of the joint probability of all four values. This is exactly the same as the procedure we went through in the last lecture ...
... So, in a domain with four variables, A, B, C, and D, the probability that variable D has value d is the sum over all possible combinations of values of the other three variables of the joint probability of all four values. This is exactly the same as the procedure we went through in the last lecture ...
Fast (Diagonally) Downward
... their value in essentially arbitrary ways without further conditions on other state variables. For example, state variables which encode vehicle locations in transportation domains such as L OGISTICS or D EPOTS never have causal dependencies on other state variables in the task (i. e., they are sour ...
... their value in essentially arbitrary ways without further conditions on other state variables. For example, state variables which encode vehicle locations in transportation domains such as L OGISTICS or D EPOTS never have causal dependencies on other state variables in the task (i. e., they are sour ...
Multi-Objective POMDPs with Lexicographic Reward Preferences
... b1 psn |b, a, ωqsT . The belief state is a sufficient statistic for a history. Note the belief does not depend on the reward vector. Definition 1 is a direct extension of the original LMDP definition to include partial observability, although we have chosen to omit the state-dependent orderings, whe ...
... b1 psn |b, a, ωqsT . The belief state is a sufficient statistic for a history. Note the belief does not depend on the reward vector. Definition 1 is a direct extension of the original LMDP definition to include partial observability, although we have chosen to omit the state-dependent orderings, whe ...
Preprint - University of Pennsylvania School of Arts and Sciences
... and indicated when they saw a target image by shifting gaze to a response dot on the screen. Our experimental design included four images presented in all possible combinations as a visual stimulus, and as an intended target, resulting in 16 experimental conditions. We held the target image fixed fo ...
... and indicated when they saw a target image by shifting gaze to a response dot on the screen. Our experimental design included four images presented in all possible combinations as a visual stimulus, and as an intended target, resulting in 16 experimental conditions. We held the target image fixed fo ...
Probabilistic Reasoning and the Design of Expert Systems
... 2.1 A Brief Introduction to the Bayesian Approach To this point in our presentation we have ignored an important component of expert system work. That is, in many applications the if… then… rules of the system are NOT always certain “if and only if” or cause/effect relationships. That is, many of th ...
... 2.1 A Brief Introduction to the Bayesian Approach To this point in our presentation we have ignored an important component of expert system work. That is, in many applications the if… then… rules of the system are NOT always certain “if and only if” or cause/effect relationships. That is, many of th ...
Toward General Analysis of Recursive Probability Models
... language. For expressions that do not contain distributions, the semantics (like the syntax) of the language is the same as that of the normal A-calculus. We have extended this semantics to handle distributions. A distribution may be thought of as a variable whose value will be determined randomly. ...
... language. For expressions that do not contain distributions, the semantics (like the syntax) of the language is the same as that of the normal A-calculus. We have extended this semantics to handle distributions. A distribution may be thought of as a variable whose value will be determined randomly. ...
HIERARCHICAL MODELS OF VARIANCE SOURCES Harri Valpola
... After 10,000 iterations, pruning was applied to variance neurons instead of the parameters of the linear mappings. The three surviving variance sources are shown in Fig. 9. None of the conventional sources lost all their out-going connections and they survived even when pruning was applied to them d ...
... After 10,000 iterations, pruning was applied to variance neurons instead of the parameters of the linear mappings. The three surviving variance sources are shown in Fig. 9. None of the conventional sources lost all their out-going connections and they survived even when pruning was applied to them d ...
Future and Emerging Technologies FET
... defining environment types, and the Semantic Sensor Network ontology (SSN) (Compton, Barnaghi, & Bermudez, 2011) which represents the domain knowledge in sensor networks. The KR technologies we propose are capable of integrating with other external knowledge sources. (C) KR Strategy 2 - Under invest ...
... defining environment types, and the Semantic Sensor Network ontology (SSN) (Compton, Barnaghi, & Bermudez, 2011) which represents the domain knowledge in sensor networks. The KR technologies we propose are capable of integrating with other external knowledge sources. (C) KR Strategy 2 - Under invest ...
A Framework for Comparing Alternative Formalisms for
... will often probability theory. have the side effect of invalidating other intuitive properties, it may still be useful to focus on the primary property violation that best captures the rationale behind the creation of the method. ...
... will often probability theory. have the side effect of invalidating other intuitive properties, it may still be useful to focus on the primary property violation that best captures the rationale behind the creation of the method. ...
modeling dynamical systems by means of dynamic bayesian networks
... While Bayesian networks are powerful tool for representing uncertainty, they do not provide direct mechanism for representing temporal dependencies. Most of the events that we meet in our everyday life are not detected based on a particular point in time. They can be described through the multiple s ...
... While Bayesian networks are powerful tool for representing uncertainty, they do not provide direct mechanism for representing temporal dependencies. Most of the events that we meet in our everyday life are not detected based on a particular point in time. They can be described through the multiple s ...
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... Associated with any undirected graphical model [1] is the so-called density of states, a term borrowed from statistical physics indicating a distribution that, for any likelihood value, gives the number of configurations with that probability. The density of states plays an important role in statist ...
... Associated with any undirected graphical model [1] is the so-called density of states, a term borrowed from statistical physics indicating a distribution that, for any likelihood value, gives the number of configurations with that probability. The density of states plays an important role in statist ...
Getting More Out of the Exposed Structure in Constraint
... constraints forming a partition of the variables and multiplying the results, thus obtaining an upper bound (Pesant 2005). ...
... constraints forming a partition of the variables and multiplying the results, thus obtaining an upper bound (Pesant 2005). ...
On the connections between backdoors, restarts, and heavy
... SAT solver, after setting the independent variables, can effectively uncover the remaining variable dependencies). In practice, backdoors can still be quite a bit smaller than the set of independent variables. For example, in the logistics planning domain, the set of independent variables is given b ...
... SAT solver, after setting the independent variables, can effectively uncover the remaining variable dependencies). In practice, backdoors can still be quite a bit smaller than the set of independent variables. For example, in the logistics planning domain, the set of independent variables is given b ...
Energy-Based Models for Sparse Overcomplete Representations
... density model and it retains the computationally convenient property that the features are a deterministic function of the observation vector. However, it abandons the marginal independence of the features (which is why we do not call them sources). A useful way of understanding the difference betwe ...
... density model and it retains the computationally convenient property that the features are a deterministic function of the observation vector. However, it abandons the marginal independence of the features (which is why we do not call them sources). A useful way of understanding the difference betwe ...
An Auxiliary System for Medical Diagnosis Based on Bayesian
... based on Bayesian belief networks, specific for medical diagnosis. The main feature of the proposed system is to provide a simple and integrated tool for designing diagnostic applications. With an easy-to-use interface, this tool gives the users (experts in the medical domain) the possibility to des ...
... based on Bayesian belief networks, specific for medical diagnosis. The main feature of the proposed system is to provide a simple and integrated tool for designing diagnostic applications. With an easy-to-use interface, this tool gives the users (experts in the medical domain) the possibility to des ...
Central Limit Theorems for Conditional Markov Chains
... On the theoretical side, studying Central Limit Theorems gives great insight into the dependency structure of time series and helps to understand long-memory effects. In accordance with the results in (Sinn and Chen, 2012), the main finding in this regard is the importance of the tail distribution o ...
... On the theoretical side, studying Central Limit Theorems gives great insight into the dependency structure of time series and helps to understand long-memory effects. In accordance with the results in (Sinn and Chen, 2012), the main finding in this regard is the importance of the tail distribution o ...
Counting Belief Propagation
... and Domingos [17]. Actually, an investigation of their approach was the seed that grew into our proposal we present in this paper. Singla and Domingos’s lifted first-order belief propagation (LFOBP) builds upon [7] and also groups random variables, i.e., nodes that send and receive identical message ...
... and Domingos [17]. Actually, an investigation of their approach was the seed that grew into our proposal we present in this paper. Singla and Domingos’s lifted first-order belief propagation (LFOBP) builds upon [7] and also groups random variables, i.e., nodes that send and receive identical message ...