"AI systems–like people–must often act despite partial and uncertain information. First, the information received may be unreliable (e.g., a patient may mis-remember when a disease started, or may not have noticed a symptom that is important to a diagnosis). In addition, rules connecting real-world events can never include all the factors that might determine whether their conclusions really apply (e.g., the correctness of basing a diagnosis on a lab test depends whether there were conditions that might have caused a false positive, on the test being done correctly, on the results being associated with the right patient, etc.) Thus in order to draw useful conclusions, AI systems must be able to reason about the probability of events, given their current knowledge."
– from David Leake, Reasoning Under Uncertainty
AMIDST provides tailored parallel (powered by Java 8 Streams) and distributed (powered by Flink or Spark) implementations of Bayesian parameter learning for batch and streaming data. Dynamic Bayesian networks: Code Examples includes some source code examples of functionalities related to Dynamic Bayesian networks. FlinkLink: Code Examples includes some source code examples of functionalities related to the module that integrates Apache Flink with AMIDST. As an example, the following figure shows how the data processing capacity of our toolbox increases given the number of CPU cores when learning an a probabilistic model (including a class variable C, two latent variables (dashed nodes), multinomial (blue nodes) and Gaussian (green nodes) observable variables) using the AMIDST's learning engine.
New statistics or fake data science textbooks are published every week but with the exact same technical content: KNN clustering, logistic regression, naive Bayes, decision and boosted trees, SVM, Bayesian statistics, centroid clustering, linear discrimination - as in the early eighties, applied to tiny data such as Fisher's iris data set. If you compare traffic statistics (Alexa rank) from top traditional statistics websites, with data science websites, the contrast is surprising. These numbers are based on Alexa rankings, which are notoriously inaccurate, though over time, they have improved their statistical science to measure and filter Internet traffic, and the numbers that I quote here have been stable recently, showing the same trend for months, and subject to a small 30% error rate (compared to 100% error rate a few years ago, based on comparing Alexa variances over time for multiple websites that we own and for which we know exact traffic stats after filtering out robots). Modern statistical data science techniques are far more robust than traditional statistics, and designed for big data.
Manipulate and analyze data that is too big to fit in memory. Perform support vector machine (SVM) and Naive Bayes classification, create bags of decision trees, and fit lasso regression on out-of-memory data. Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters. Develop clients for MATLAB Production Server in any programming language that supports HTTP.
In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques. Some of the fundamental Statistical and Probability Theory needed for ML are Combinatorics, Probability Rules & Axioms, Bayes' Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.
In recent years, the term'machine learning' has become very popular among developers and business alike, even though research in the field has been going on for decades. Essentially, machine learning is about teaching machines to learn concepts and techniques the way humans do. While computer scientists were making huge strides in increasing computational performance by utilizing advancements in hardware to enable machines to solve complex calculations, hypotheses by their fellow researchers from AI on the ability of machines to think and act like humans were met with skepticism. A sub-field of AI, machine learning, saw rapid growth when companies such as Google and Facebook began to find new ways to utilize the troves of data for more profit.
We present CGO-AS, a generalized Ant System (AS) implemented in the framework of Cooperative Group Optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning.
The first challenge is that Solomonoff's approach is purely deterministic, the coded models produce a string output and if the string output doesn't exactly match the target output then the model is considered wrong. In this way when calculating the priors I consider the prior probability of the family of models; there is no prior over the parameters, only a prior probability for the type of model. It's best to use a low-level programming language, a high level language favours complex models. The posterior probability of the constant model dropped to 99.9% of its prior value, the inverse model's probability remained about the same and the remaining models; probability approximately doubled.
In sum, he argues that when the sample size is small (which happens a lot in the bio domain), linear models with few parameters perform better than deep nets even with a modicum of layers and hidden units. In essence, every time that you do some form of numerical optimization, you're performing some Bayesian inference with particular assumptions and priors. On the other hand, if you decrease the learning parameter, the Markov chain slowly approximates narrower minima until it converges in a tight region; that is, you increase the bias for a certain region. Another parameter, the batch size in SGD, also controls what type of region the algorithm converges two: wider regions for small batches and sharper regions with larger batches.
Paul Bilokon, founder of Thalesians, an organisation to promote deeper thinking and philosophy within finance, points out that many non-financial systems are using software techniques that are far ahead. Paul will be speaking about new infrastructure and showing off some machine learning libraries at the forthcoming IBT data science and capital markets event. Advances in optimisation are being driven by techniques like Bayesian Learning, complemented by technological advances in terms of infrastructure; projects like Apache Spark; kdb and q language. That's because they use Bayesian learning methods to update information on behavioural trends.
Since there are 25 long haired women and 2 long haired men, guessing that the ticket owner is a woman is a safe bet. To lay our foundation, we need to quickly mention four concepts: probabilities, conditional probabilities, joint probabilities and marginal probabilities. The probability of a thing happening is the number of ways that thing can happen divided by the total number of things that can happen. Combining these by multiplication gives the joint probability, P(woman with short hair) P(woman) * P(short hair woman).