Difference between revisions of "Team:Tongji China/Model"
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We use Bayesian statistics to predict which type of mutation is most likely to product MHC strong binding peptides with the sum of affinity of each mutation site and each allele type.<br> | We use Bayesian statistics to predict which type of mutation is most likely to product MHC strong binding peptides with the sum of affinity of each mutation site and each allele type.<br> | ||
Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation that views probability as the limit of the relative frequency of an event after a large number of trials.<br>Bayes' theorem is a fundamental theorem in Bayesian statistics, as it is used by Bayesian methods to update probabilities, which are degrees of belief, after obtaining new data. Given two events A and B, the conditional probability of A given that B is true is expressed as follows:<br> | Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation that views probability as the limit of the relative frequency of an event after a large number of trials.<br>Bayes' theorem is a fundamental theorem in Bayesian statistics, as it is used by Bayesian methods to update probabilities, which are degrees of belief, after obtaining new data. Given two events A and B, the conditional probability of A given that B is true is expressed as follows:<br> | ||
| − | <p style="text-align:center"><img src="https://static.igem.org/mediawiki/2018/ | + | <p style="text-align:center"><img src="https://static.igem.org/mediawiki/2018/8/82/T--Tongji_China--picture-drylab-model-1.png" width="90%" height="90%"></p> |
Revision as of 03:51, 9 October 2018
Dry Lab
Model
Acknowledge：CPU China. This part is made by Team CPU China and thanks for their collaboration!
We use Bayesian statistics to predict which type of mutation is most likely to product MHC strong binding peptides with the sum of affinity of each mutation site and each allele type.Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation that views probability as the limit of the relative frequency of an event after a large number of trials.
Bayes' theorem is a fundamental theorem in Bayesian statistics, as it is used by Bayesian methods to update probabilities, which are degrees of belief, after obtaining new data. Given two events A and B, the conditional probability of A given that B is true is expressed as follows:
