[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]

@probnstat Avatar @probnstat Probability and Statistics

Probability and Statistics posts on X about inference, nets, math, events the most. They currently have XXXXXX followers and XX posts still getting attention that total XXXXX engagements in the last XX hours.

Engagements: XXXXX #

Engagements Line Chart

Mentions: XX #

Mentions Line Chart

Followers: XXXXXX #

Followers Line Chart

CreatorRank: XXXXXXX #

CreatorRank Line Chart

Social Influence

Social category influence currencies XXXX%

Social topic influence inference #12, nets 3.13%, math 3.13%, events 3.13%, represent 3.13%, virtual 3.13%, vr 3.13%, theory 3.13%, future 3.13%, longterm XXXX%

Top accounts mentioned or mentioned by @spinar_d @marketmoves_lr @harmonicmath @grok @skoularidou @william_r2rclub @iammteah @zaidom5 @lukaacvetkovic @bitsadventures @santihcg @fact_patrol @tomislav_rupic @karanjagtiani04 @brainvomit143

Top Social Posts

Top posts by engagements in the last XX hours

"Information-theoretic probability links uncertainty with measures like entropy mutual information and KL divergence to quantify how much information a random variable provides. In probability and statistics it refines inference and model selection. In ML it drives loss functions feature selection and compression in deep nets. In real life it powers data transmission cybersecurity error-correcting codes image/audio compression and decision-making under uncertainty. Image:"
X Link 2025-11-30T12:16Z 75.1K followers, 27.1K engagements

"A Neural Processing Unit (NPU) is a specialized chip optimized for fast parallel computation especially matrix and vector operations. In probability and statistics NPUs accelerate tasks like Monte Carlo simulations and Bayesian inference. In machine learning they speed up neural network training and inference with low power use. In real life NPUs power features such as face unlock speech recognition smart cameras autonomous driving and on-device predictive AI across phones cars and IoT devices"
X Link 2025-11-29T19:25Z 75.1K followers, 30.8K engagements

"TRUE or FALSE: If P(A B) = X then A and B must be equal"
X Link 2025-11-30T15:13Z 75.1K followers, 112.4K engagements

"Discrete math underpins probability machine learning and daily life by providing the language of sets graphs logic and combinatorics. It shapes probability through counting events and random variables; powers ML via algorithms optimization and state-space modeling; and appears in real life in network routing scheduling cryptography and decision-making. Its structures let us model uncertainty design efficient systems and solve complex problems with clarity"
X Link 2025-12-06T12:12Z 75.1K followers, 13.6K engagements

"Neural Radiance Fields (NeRFs) use neural networks to represent a 3D scene as a continuous volume of density and color rather than a mesh of polygons. By learning from a set of 2D photos a NeRF can synthesize photorealistic novel views of the scene from any angle using ray marching. In real life this technology is revolutionizing virtual reality (VR) allowing for immersive exploration of captured real-world spaces and is transforming special effects in movies by enabling easy 3D reconstruction from standard video footage. Image:"
X Link 2025-11-27T12:38Z 75.1K followers, 10.2K engagements

"Ergodic theory studies systems whose long-term average behavior matches their average across all possible states. Mixing processes strengthen this by ensuring future states become independent of past states over time. In probability and statistics these ideas justify the law of large numbers and stable parameter estimation. In ML they support stochastic gradient methods and Markov chain sampling. In real life they model weather traffic finance and dynamics of social and biological systems"
X Link 2025-11-30T11:20Z 75K followers, 25.4K engagements

"Gibbs sampling is a Markov chain Monte Carlo method that generates samples from complex joint distributions by iteratively sampling each variable from its conditional distribution. In probability and statistics it enables Bayesian inference when direct sampling is hard. In ML it supports latent variable models topic modeling and generative modeling. In real life Gibbs sampling helps in genetic data analysis image restoration recommendation systems and forecasting in social and economic networks"
X Link 2025-12-02T17:12Z 74.9K followers, 9705 engagements

"Which field of study do you think has the highest ROI"
X Link 2025-12-02T18:59Z 75.1K followers, 15.2K engagements

"TRUE or FALSE: If two continuous random variables are independent then the most likely value (the mode) of their sum is equal to the sum of their individual modes"
X Link 2025-12-05T20:48Z 74.9K followers, 6575 engagements

"Batch reinforcement learning trains agents using fixed pre-collected datasets instead of online interaction. It emphasizes stability and safety because the data may not cover all situations. In probability and statistics it uses uncertainty estimation and off-policy evaluation. In ML it powers decision systems where exploration is risky like healthcare finance and recommender systems. In real life batch RL supports treatment planning portfolio allocation industrial automation and personalized digital services"
X Link 2025-12-02T13:33Z 75.1K followers, 4813 engagements

"Inverse reinforcement learning (IRL) aims to recover the reward function that explains an experts behavior rather than learning a policy directly. By observing actions IRL infers what agents value. In probability and statistics it uses Bayesian inference and likelihood models to estimate hidden preferences. In ML it powers autonomous driving robotics and human-AI alignment by enabling machines to learn from demonstrations. In real life IRL helps model consumer decisions healthcare routines and strategic behavior in economics and security. Image:"
X Link 2025-12-02T10:43Z 75.1K followers, 7162 engagements

"TIME SERIE & QUANTUM ML: Quantum machine-learning algorithms have recently gained strong interest on photonic platforms. Reconfigurable integrated photonic circuits are especially promising because adaptive feedback enables the nonlinear behavior needed for neural-networklike processing. We implement a quantum reservoir-computing protocol using a reconfigurable linear optical circuit with single-photon detection. A multiphoton setup performs time-series forecasting by encoding inputs in an optical phase that modulates the reservoir state. Output probabilities set feedback phases and are"
X Link 2025-12-03T07:02Z 75.1K followers, 8477 engagements

"Quantum physics principles are fundamental to many real-life technologies including lasers MRI scanners and the transistors that power all modern electronics. In the realm of artificial intelligence quantum machine learning (QML) is an emerging field that harnesses quantum phenomena like superposition and entanglement. This allows for the processing of vast datasets and the solving of complex problems much faster than classical computers. QML has the potential to revolutionize areas such as drug discovery by simulating molecules optimizing financial models and enhancing pattern recognition"
X Link 2025-10-04T20:36Z 75.1K followers, 2.1M engagements

"SGD with Momentum accelerates optimization by accumulating velocity from past updates like a heavy ball rolling down a hill. Instead of zig-zagging inefficiently it builds speed in the right direction helping models plow through flat areas and escape local minima. In deep learning it's a standard tool to train massive neural networks faster and more stably. This efficiency powers real-life breakthroughs like self-driving cars voice assistants and AlphaFold"
X Link 2025-11-27T07:34Z 75.1K followers, 16.2K engagements

"A Mercer kernel is a symmetric positive-definite function that defines an inner product in a high-dimensional feature space without requiring explicit feature mapping. This property allows complex relationships to be captured through simple kernel evaluations. In probability Mercer kernels support kernel density estimation kernel mean embeddings and nonparametric inference. In machine learning they form the core of support vector machines Gaussian processes kernel PCA and many regression and classification methods enabling nonlinear patterns to be learned using linear algorithms in"
X Link 2025-12-08T17:21Z 75.1K followers, 8519 engagements