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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Probability Laws01:49

Probability Laws

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Overview
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Related Experiment Videos

Probability Statements Extraction with Constrained Conditional Random Fields.

Léa A Deleris1, Charles Jochim1

  • 1IBM Research - Ireland.

Studies in Health Technology and Informatics
|September 1, 2016
PubMed
Summary
This summary is machine-generated.

This study improves extracting probability statements from medical papers by using domain knowledge to constrain classification models. This method significantly reduces errors compared to traditional approaches.

Related Experiment Videos

Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Computational Linguistics

Background:

  • Extracting structured information from unstructured medical text is challenging.
  • Previous classification methods for probability statements resulted in many false negatives.
  • Conditional Random Field (CRF) models offer a framework for sequence labeling tasks.

Purpose of the Study:

  • To enhance the extraction of probability statements from academic medical literature.
  • To address the limitations of traditional classification methods, specifically high false negative rates.
  • To investigate the impact of incorporating domain knowledge constraints into CRF models.

Main Methods:

  • Utilized a Conditional Random Field (CRF) model for sequence labeling.
  • Implemented domain knowledge constraints to refine the CRF model's output.
  • Evaluated performance based on standard metrics for information extraction.

Main Results:

  • Constraining the CRF model's output with domain knowledge led to a significant performance improvement.
  • The proposed method demonstrated a reduction in false negatives compared to unconstrained approaches.
  • The findings highlight the benefit of integrating external knowledge into machine learning models for medical text analysis.

Conclusions:

  • Constraining CRF models with domain knowledge is an effective strategy for improving the accuracy of probability statement extraction from medical papers.
  • This approach offers a promising direction for more reliable automated analysis of clinical literature.
  • Future work could explore different types of domain knowledge and advanced constraint mechanisms.