2015年3月26日 · Probabilistic means there is uncertainty in the process where the possible outcomes of some event may or may not have fair (equal) shares. For example: When you throw two fair dice, the probability of getting a sum of the observed top faces greater then 4, is greater than getting a sum of less or equal to 4.

2016年11月2日 · Probabilistic inference uses probabilistic models, i.e. models that describe the statistical problems in terms of probability theory and probability distributions. While statistics use probability theory quite heavily, you cannot say that those two disciplines are the same thing (check the discussion in this thread ).

2011年3月20日 · One could say that statistics comprises of two parts. The first part is the question of how to formulate and evaluate probabilistic models for the problem; this endeavor lies within the domain of "philosophy of science". The second part is the question of obtaining answers after a certain model has been assumed.

2022年7月6日 · 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).

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.

2017年2月7日 · The term "probabilistic approach" means that the inference and reasoning taught in your class will be rooted in the mature field of probability theory. That term is often (but not always) synonymous with "Bayesian" approaches, so if you have had any exposure to Bayesian inference you should have no problems picking up on the probabilistic approach.

2016年10月17日 · The goal of PPCA is not to give better results than PCA, but to permit a broad range of future extensions and analysis.

2019年8月9日 · 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

2018年5月18日 · Edit 2: it seems that probabilistic is getting popular for time series modeling, where deep learning doesn't seem as effective as in other domains. Edit 3 (December 2020): probabilistic programming is now becoming much more popular in a wide variety of domains, as probabilistic programming languages get better and the solvers get better and faster.

2013年8月11日 · I haven't noticed an aversion among Bayesians to talking about statistical modeling. My impression is that probabilistic modeling is the more likely phrase if authors think of themselves as probabilists and have little or no concern with estimation or inference using real data, and conversely for statisticians and data-sensitive texts.