1. The document presents a conjecture about the error probability of overestimating the true order k* when learning autoregressive moving average (ARMA) models from samples.
2. The conjecture states that if the estimated order k is greater than the true order k*, the error probability is equal to the probability that a chi-squared distributed random variable with k - k* degrees of freedom is greater than (k - k*)dn, where dn is related to the sample size n.
3. The author provides evidence that a sum of squared estimated ARMA coefficients could be chi-squared distributed, lending credibility to the conjecture.
On Twisted Paraproducts and some other Multilinear Singular IntegralsVjekoslavKovac1
Presentation.
9th International Conference on Harmonic Analysis and Partial Differential Equations, El Escorial, June 12, 2012.
The 24th International Conference on Operator Theory, Timisoara, July 3, 2012.
Bayesian network structure estimation based on the Bayesian/MDL criteria when...Joe Suzuki
J. Suzuki. ``Bayesian network structure estimation based on the Bayesian/MDL criteria when both discrete and continuous variables are present". IEEE Data Compression Conference, pp. 307-316, Snowbird, Utah, April 2012.
On Twisted Paraproducts and some other Multilinear Singular IntegralsVjekoslavKovac1
Presentation.
9th International Conference on Harmonic Analysis and Partial Differential Equations, El Escorial, June 12, 2012.
The 24th International Conference on Operator Theory, Timisoara, July 3, 2012.
Bayesian network structure estimation based on the Bayesian/MDL criteria when...Joe Suzuki
J. Suzuki. ``Bayesian network structure estimation based on the Bayesian/MDL criteria when both discrete and continuous variables are present". IEEE Data Compression Conference, pp. 307-316, Snowbird, Utah, April 2012.
Field Induced Josephson Junction (FIJJ) is defined as the physical system made by placement of ferromagnetic strip directly or indirectly [insulator layer in-between] on the top of superconducting strip [3, 4, 7]. The analysis conducted in extended Ginzburg-Landau, Bogoliubov-de Gennes and RCSJ [11] models essentially points that the system is in most case a weak-link Josephson junction [2] and sometimes has features of tunneling Josephson junction [1]. Generalization of Field Induced Josephson junctions leads to the case of network of robust coupled field induced Josephson junctions [4] that interact in inductive way. Also the scheme of superconducting Random Access Memory (RAM) for Rapid Single Flux [8, 9] quantum (RSFQ) computer is drawn [6, 10] using the concept of tunneling Josephson junction [1] and Field Induced Josephson junction [3, 4].
The given presentation is also available by YouTube (https://www.youtube.com/watch?v=uIqXqiwDsSM).
Literature
[1]. B.D.Josephson, Possible new effects in superconductive tunnelling, PL, Vol.1, No. 251, 1962
[2]. K.Likharev, Josephson junctions Superconducting weak links, RMP, Vol. 51, No. 101, 1979
[3]. K.Pomorski and P.Prokopow, Possible existence of field induced Josephson junctions, PSS B, Vol.249, No.9, 2012
[4]. K.Pomorski, PhD thesis: Physical description of unconventional Josephson junction, Jagiellonian University, 2015
[4]. K.Pomorski, H.Akaike, A.Fujimaki, Towards robust coupled field induced Josephson junctions, arxiv:1607.05013, 2016
[6]. K.Pomorski, H.Akaike, A.Fujimaki, Relaxation method in description of RAM memory cell in RSFQ computer, Procedings of Applied Conference 2016 (in progress)
[7]. J.Gelhausen and M.Eschrig, Theory of a weak-link superconductor-ferromagnet Josephson structure, PRB, Vol.94, 2016
[8]. K.K. Likharev, Rapid Single Flux Quantum Logic (http://pavel.physics.sunysb.edu/RSFQ/Research/WhatIs/rsfqre2m.html)
[9]. Proceedings of Applied Superconductivity Confence 2016, plenary talk by N.Yoshikawa, Low-energy high-performance computing based on superconducting technology (http://ieeecsc.org/pages/plenary-series-applied-superconductivity-conference-2016-asc-2016#Plenary7)
[10]. A.Y.Herr and Q.P.Herr, Josephson magnetic random access memory system and method, International patent nr:8 270 209 B2, 2012
[11]. J.A.Blackburn, M.Cirillo, N.Gronbech-Jensen, A survey of classical and quantum interpretations of experiments on Josephson junctions at very low temperatures, arXiv:1602.05316v1, 2016
Hecke Operators on Jacobi Forms of Lattice Index and the Relation to Elliptic...Ali Ajouz
Jacobi forms of lattice index, whose theory can be viewed as extension of the theory of classical Jacobi forms, play an important role in various theories, like the theory of orthogonal modular forms or the theory of vertex operator
algebras. Every Jacobi form of lattice index has a theta expansion which implies, for index of odd rank, a connection to half integral weight modular forms and then via Shimura lifting to modular forms of integral weight, and implies a direct connection to modular forms of integral weight if the rank is
even. The aim of this thesis is to develop a Hecke theory for Jacobi forms of lattice index extending the Hecke theory for the classical Jacobi forms, and to study how the indicated relations to elliptic modular forms behave under Hecke operators. After defining Hecke operators as double coset operators,
we determine their action on the Fourier coefficients of Jacobi forms, and we determine the multiplicative relations satisfied by the Hecke operators, i.e. we study the structural constants of the algebra generated by the Hecke operators. As a consequence we show that the vector space of Jacobi forms
of lattice index has a basis consisting of simultaneous eigenforms for our Hecke operators, and we discover the precise relation between our Hecke algebras and the Hecke algebras for modular forms of integral weight. The
latter supports the expectation that there exist equivariant isomorphisms between spaces of Jacobi forms of lattice index and spaces of integral weight modular forms. We make this precise and prove the existence of such liftings
in certain cases. Moreover, we give further evidence for the existence of such liftings in general by studying numerical examples.
zkStudyClub - cqlin: Efficient linear operations on KZG commitments Alex Pruden
This week, Liam Eagen (Blockstream Research) and Ariel Gabizon (Zeta Function Technologies) present cqlin - Efficient linear operations on KZG commitments with cached quotients.
Given two KZG-committed polynomials , a matrix , and subgroup of order , we present a protocol for checking that . After preprocessing, the prover makes field and group operations. This presents a significant improvement over the lincheck protocols in [CHMMVW, COS], where the prover's run-time (also after preprocessing) was quasilinear in the number of non-zeroes of M, which could be n^2.
Forest Learning based on the Chow-Liu Algorithm and its Application to Genom...Joe Suzuki
J. Suzuki, ``Forest Learning based on the Chow-Liu Algorithm and its Application to Genome Differential Analysis: A Novel Mutual Information Estimation", AMBN 2015, Yokohama, Japan
Structure Learning of Bayesian Networks with p Nodes from n Samples when n<...Joe Suzuki
``Structure Learning of Bayesian Networks with p Nodes from n Samples when n<<p">, presented at Probabilistic Graphical Model Workshop, ISM, March 2016.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Lateral Ventricles.pdf very easy good diagrams comprehensive
A Conjecture on Strongly Consistent Learning
1. .
.
A Conjecture on Strongly Consistent Learning
Joe Suzuki
Osaka University
July 7, 2009
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 1 / 21
2. Outline
Outline
1 Stochastic Learning with Model Selection
2 Learning CP
3 Learning ARMA
4 Conjecture
5 Summary
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 2 / 21
3. Stochastic Learning with Model Selection
Probability Space (Ω, F, µ)
Ω: the entire set
F: the set of events over Ω
µ : F → [0, 1]: a probability measure (µ(Ω) = 1)
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 3 / 21
4. Stochastic Learning with Model Selection
Stochastic Learning
X : Ω → R: random variable
µX : the measure w.r.t. X
x1, · · · , xn ∈ X(Ω): n samples
Induction/Inference
.
µX −→ x1, · · · , xn (Random Number Generation)
x1, · · · , xn −→ µX (Stochastic Learning)
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 4 / 21
5. Stochastic Learning with Model Selection
Two Problems with Model Selection
Conditional Probability for Y given X
.
.
µY |X
Identify the equivalence relation in X from samples.
x ∼ x′
⇐⇒ µ(Y = y|X = x) = µ(Y = y|X = x′
) for y ∈ Y (Ω)
ARMA for {Xn}∞
n=−∞
.
Xn +
∑k
j=1 λj Xn−j ∼ N(0, σ2) with {λ}k
j=1 (0 ≤ k < ∞)
Identify the true order k from samples.
This paper
compares CP and ARMA to find how close they are.
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 5 / 21
6. Learning CP
CP
A ∈ F
G ⊆ F
CP of A given G (Radon-Nykodim)
.
.
∃G-measurable g : Ω → R s.t µ(A ∩ G) =
∫
G
g(ω)µ(dω) for G ∈ G.
CP µY |X
A := (Y = y), y ∈ Y (Ω)
G: generated by subsets of X.
Assumption
|Y (Ω)| < ∞
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 6 / 21
7. Learning CP
Applications for CP
Stochastic Decision Trees, Stochastic Horn Clauseses Y |X, etc.
.
.
Find G from samples.
G = {G1, G2, G3, G4, G5}
"!
#
"!
#
"!
#
"!
#
"!
#
"!
#
"!
#
"!
#
¡
¡¡
e
ee
¡
¡¡
e
ee
¨¨¨¨¨¨
rrrrrr
Weather
Temp
G3
Wind
G1 G2 G4 G5
Clear
Cloud
Rain
≤ 75 > 75 Yes No
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 7 / 21
9. Learning CP
Learning CP
zi := (xi , yi ) ∈ Z(Ω) := X(Ω) × Y (Ω)
From zn := (z1, · · · , zn) ∈ Zn(Ω), find a minimal G.
G∗: true
ˆGn: estimated
Assumption
.
.
|G∗| ∞
Two Types of Errors
G∗ ⊂ ˆGn (Over Estimation)
G∗ ̸⊆ ˆGn (Under Estimation)
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 9 / 21
10. Learning CP
Example: Quinlan’s Q4.5
(a) G∗
¡¡ ee ¡¡ ee
¨¨¨¨
rrrr
Weather
Temp
3
Wind
1 2 4 5
Clear
Cloud
Rain
≤ 75 75 Yes No
(b) Overestimation
¡¡ ee ¡¡ ee
¨¨¨¨
rrrr
Weather
Temp
Wind
Wind
1 2 5 6
3 4
Clear
Cloud
Rain
≤ 75 75 Yes No
Yes No
¡¡ ee
(c) Underestimation
¡¡ ee
¨¨¨¨
rrrr
Weather
Temp
3
4
1 2
Clear
Cloud
Rain
≤ 75 75
(d) Underestimation
¨¨¨¨
rrrr
Weather
1
Wind
Wind
4 5
¡¡ ee
¡¡ ee
2 3
Clear
Cloud
Rain
Yes No
¡¡ ee
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 10 / 21
11. Learning CP
Information Criteria for CP
Given zn ∈ Z(Ω), find G minimizing
I(G, zn
) := H(G, zn
) +
k(G)
2
dn
H(G, zn): Empirical Entropy (Fitness of zn to G)
k(G): the # of Parameters (Simplicity of G)
dn ≥ 0:
dn
n
→ 0
dn = log n BIC/MDL
dn = 2 AIC
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 11 / 21
12. Learning CP
Consistency
Consistency ( ˆGn −→ G∗ (n → ∞))
.
.
Weak Consistency Probability Convergence (O(1) dn o(n))
Strong Consistency Almost Surely Convergence (MDL/BIC etc.)
AIC (dn = 2) is not consistent because {dn} is too small !
Problem
What is the minimum {dn} for Strong Consistency ?
Answer (Suzuki, 2006)
dn = 2 log log n
(the Law of Iterated Logarithms)
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 12 / 21
13. Learning CP
Error Probability for CP
G∗ ⊂ G (Over Estimation)
.
.
µ{ω ∈ Ω|I(G, Zn
(ω)) I(G∗
, Zn
(ω))}
= µ{ω ∈ Ω|χ2
K(G)−K(G∗)(ω) (K(G) − K(G∗
))dn}
χ2
K(G)−K(G∗) ∼ χ2 of freedom K(G) − K(G∗)
G∗ ̸⊆ G (Under Estimation)
µ{ω ∈ Ω|I(G, Zn
(ω)) I(G∗
, Zn
(ω))
diminishes exponentially with n
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 13 / 21
14. Learning CP
Applications for ARMA
Time Series Xi |Xi−k, · · · , Xi−1
.
.
Xi +
k∑
j=1
λj Xi−j ∼ N(0, σ2
)
Find k from samples.
Gaussian Bayesian Networks Xi |Xj , j ∈ π(i)
Xi +
∑
j∈π(i)
λj,i Xj ∼ N(0, σ2
i )
Find π(i) ⊆ {1, · · · , i − 1} from samples.
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 14 / 21
15. Learning ARMA
Learning ARMA
k ≥ 0
{λj }k
j=1: λi ∈ R
σ2 ∈ R0
{Xi }∞
i=−∞: Xi +
k∑
j=1
λj Xi−j = ϵi ∼ N(0, σ2
)
Given xn := (x1, · · · , xn) ∈ X1(Ω) × · · · × Xn(Ω), estimate
k: known {λj }k
j=1, σ2
k: Unknown k as well as {λj }k
j=1, σ2
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 15 / 21
16. Learning ARMA
Yule-Walker
If k is known, solve {ˆλj,k}k
j=1 and ˆσ2
k w.r.t.
¯x :=
1
n
n∑
i=1
xi
cj :=
1
n
n−j∑
i=1
(xi − ¯x)(xi+j − ¯x) , j = 0, · · · , k
−1 c1 c2 · · · ck
0 c0 c1 · · · ck−1
0 c1 c0 · · · ck−2
...
...
...
...
...
0 ck−1 ck−2 · · · c0
ˆσ2
k
ˆλ1,k
ˆλ2,k
...
ˆλk,k
=
−c0
−c1
−c2
...
−ck
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 16 / 21
17. Learning ARMA
Information Criteria for ARMA
If k is unknown, given xn ∈ Xn(Ω), find k minimiziing
I(k, xn
) :=
1
2
log ˆσ2
k +
k
2
dn
ˆσ2
k: obtain using Yule-Walker
dn ≥ 0:
dn
n
→ 0
dn = log n BIC/MDL
dn = 2 AIC
dn = 2 log log n Hannan-Quinn (1979)
=⇒the minimum {dn} satisfying Strong Consistency
Suzuki (2006) was inspired by Hannan-Quinn (1979) !
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 17 / 21
18. Learning ARMA
Error Probability for ARMA
k∗: true Order
k∗ k (Under Estimation)
.
µ{ω ∈ Ω|I(k, Xn
(ω)) I(k∗, Xn
(ω))}
diminishes exponentially with n
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 18 / 21
19. Conjecture
Error Probability for ARMA (cont’d)
Conjecture: k∗ k (Over Estimation)
.
µ{ω ∈ Ω|I(k, Xn
(ω)) I(k∗, Xn
(ω))}
= µ{ω ∈ Ω|χ2
k−k∗
(ω) (k − k∗)dn}
χ2
k−k∗
∼ χ2 of freedom k − k∗
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 19 / 21
20. Conjecture
In fact, ...
For k k∗ and large n (use ˆσ2
k = (1 − ˆλ2
k,k)σ2
k−1),
1
2
log ˆσ2
k +
k
2
dn
k∗
2
log σ2
k∗
+
k∗
2
dn
⇐⇒
k∑
j=k∗+1
log
ˆσ2
j
ˆσ2
j−1
(k − k∗)dn
⇐⇒
k∑
j=k∗+1
log(1 − ˆλ2
k,k) dn
⇐⇒
k∑
j=k∗+1
ˆλ2
k,k (k − k∗)dn
It is very likely that
∑k
j=k∗+1
ˆλ2
k,k ∼ χ2
k−k∗
although ˆλk,k ̸∼ N(0, 1).
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 20 / 21
21. Summary
Summary
CP and ARMA are similar
.
.
1 Results in ARMA can be applied to CP
2 Results in Gausssian BN can be applied to finite BN.
The conjecture is likely enough ?
.
.
Answer: Perhaps. Several evidences.
{Zi }n
i=1: independent
Zi ∼ N(0, 1)
rankA = n − k, A2
= A =⇒ t
[Z1, · · · , Zn]A[Z1, · · · , Zn] ∼ χ2
n−k
Joe Suzuki (Osaka University) A Conjecture on Strongly Consistent Learning July 7, 2009 21 / 21