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probabilistic    
概率性的; 随机; 不确定性的

概率性的; 随机; 不确定性的

probabilistic
adj 1: of or relating to the Roman Catholic philosophy of
probabilism
2: of or relating to or based on probability; "probabilistic
quantum theory"


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  • What is the importance of probabilistic machine learning?
    Because probabilistic models effectively "know what they don't know", they can help prevent terrible decisions based on unfounded extrapolations from insufficient data As the questions we ask and the models we build become increasingly complex, the risks of insufficient data rise
  • Whats the difference between probability and statistics?
    The short answer to this I've heard from Persi Diaconis is the following: The problems considered by probability and statistics are inverse to each other In probability theory we consider some underlying process which has some randomness or uncertainty modeled by random variables, and we figure out what happens In statistics we observe something that has happened, and try to figure out what
  • Probabilistic vs. other approaches to machine learning
    On the other hand, from statistical points (probabilistic approach) of view, we may emphasize more on generative models For example, mixture of Gaussian Model, Bayesian Network, etc The book by Murphy "machine learning a probabilistic perspective" may give you a better idea on this branch
  • Is there any difference between Random and Probabilistic?
    It seems i can't directly say probabilistic and random are identical But this is telling : random experiment is a probabilistic experiment Is there any difference between Random and Probabili
  • Probability model vs statistical model vs stochastic model
    The term ' Probability Model ' (probabilistic model) is usually an alias for stochastic model References: 1 Using statistical methods to model the fine-tuning of molecular machines and systems Steinar Thorvaldsen, Ola Hossjer [2] Statistics (Point Estimation) - Lecture One Charlotte Wickham - Berkeley
  • What is the difference between regular PCA and probabilistic PCA . . .
    I know regular PCA does not follow probabilistic model for observed data So what is the basic difference between PCA and PPCA? In PPCA latent variable model contains for example observed variable
  • XGBoost XGBRanker to produce probabilities instead of ranking scores
    Learning-to-rank models producing relevance_scores isn't required to account for probabilities to evaluate uncertainties due to their nature Of course you could simply apply softmax to your XGBRanker output relevance_score to represent a 'normalized' ranking across a group, and note you used pairwise objective and you could further use 'eval_metric': 'ndcg' to more align with your concerned
  • What is the difference between the probabilistic and non-probabilistic . . .
    A probabilistic approach (such as Random Forest) would yield a probability distribution over a set of classes for each input sample A deterministic approach (such as SVM) does not model the distribution of classes but rather separates the feature space and return the class associated with the space where a sample originates from
  • How is the VAE encoder and decoder probabilistic?
    I think your view is correct, indeed the probabilistic nature of VAEs stems from parametrizing the latent distribution and then sampling from it I would argue that this procedure influences the whole network, making them more capable of generalization but also more prone to noisy reconstruction (often seen in GANs vs VAE comparisons) Of course, this doesn't make the rest of the network
  • What exactly is the difference between a parametric and non-parametric . . .
    Data generation model It describes our assumptions about the probabilistic distribution that generated our data From mathematical statistics we know that data generation model can be parametric or non-parametric As Glen_b pointed out, in parametric data generation model this distribution is defined by a fixed number of parameters





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